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https://paperswithcode.com/paper/how-do-source-side-monolingual-word
|
1806.01515
| null | null |
How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?
|
Using pre-trained word embeddings as input layer is a common practice in many
natural language processing (NLP) tasks, but it is largely neglected for neural
machine translation (NMT). In this paper, we conducted a systematic analysis on
the effect of using pre-trained source-side monolingual word embedding in NMT.
We compared several strategies, such as fixing or updating the embeddings
during NMT training on varying amounts of data, and we also proposed a novel
strategy called dual-embedding that blends the fixing and updating strategies.
Our results suggest that pre-trained embeddings can be helpful if properly
incorporated into NMT, especially when parallel data is limited or additional
in-domain monolingual data is readily available.
| null |
http://arxiv.org/abs/1806.01515v2
|
http://arxiv.org/pdf/1806.01515v2.pdf
| null |
[
"Shuoyang Ding",
"Kevin Duh"
] |
[
"Machine Translation",
"NMT",
"Translation",
"Word Embeddings"
] | 2018-06-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/theory-of-estimation-of-distribution
|
1806.05392
| null | null |
Theory of Estimation-of-Distribution Algorithms
|
Estimation-of-distribution algorithms (EDAs) are general metaheuristics used
in optimization that represent a more recent alternative to classical
approaches like evolutionary algorithms. In a nutshell, EDAs typically do not
directly evolve populations of search points but build probabilistic models of
promising solutions by repeatedly sampling and selecting points from the
underlying search space. Recently, there has been made significant progress in
the theoretical understanding of EDAs. This article provides an up-to-date
overview of the most commonly analyzed EDAs and the most recent theoretical
results in this area. In particular, emphasis is put on the runtime analysis of
simple univariate EDAs, including a description of typical benchmark functions
and tools for the analysis. Along the way, open problems and directions for
future research are described.
| null |
http://arxiv.org/abs/1806.05392v1
|
http://arxiv.org/pdf/1806.05392v1.pdf
| null |
[
"Martin S. Krejca",
"Carsten Witt"
] |
[
"Evolutionary Algorithms"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/convex-coupled-matrix-and-tensor-completion
|
1705.05197
| null | null |
Convex Coupled Matrix and Tensor Completion
|
We propose a set of convex low rank inducing norms for a coupled matrices and
tensors (hereafter coupled tensors), which shares information between matrices
and tensors through common modes. More specifically, we propose a mixture of
the overlapped trace norm and the latent norms with the matrix trace norm, and
then, we propose a new completion algorithm based on the proposed norms. A key
advantage of the proposed norms is that it is convex and can find a globally
optimal solution, while existing methods for coupled learning are non-convex.
Furthermore, we analyze the excess risk bounds of the completion model
regularized by our proposed norms which show that our proposed norms can
exploit the low rankness of coupled tensors leading to better bounds compared
to uncoupled norms. Through synthetic and real-world data experiments, we show
that the proposed completion algorithm compares favorably with existing
completion algorithms.
| null |
http://arxiv.org/abs/1705.05197v2
|
http://arxiv.org/pdf/1705.05197v2.pdf
| null |
[
"Kishan Wimalawarne",
"Makoto Yamada",
"Hiroshi Mamitsuka"
] |
[] | 2017-05-15T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/parameter-learning-and-change-detection-using
|
1806.05387
| null | null |
Parameter Learning and Change Detection Using a Particle Filter With Accelerated Adaptation
|
This paper presents the construction of a particle filter, which incorporates
elements inspired by genetic algorithms, in order to achieve accelerated
adaptation of the estimated posterior distribution to changes in model
parameters. Specifically, the filter is designed for the situation where the
subsequent data in online sequential filtering does not match the model
posterior filtered based on data up to a current point in time. The examples
considered encompass parameter regime shifts and stochastic volatility. The
filter adapts to regime shifts extremely rapidly and delivers a clear heuristic
for distinguishing between regime shifts and stochastic volatility, even though
the model dynamics assumed by the filter exhibit neither of those features.
| null |
http://arxiv.org/abs/1806.05387v1
|
http://arxiv.org/pdf/1806.05387v1.pdf
| null |
[
"Karol Gellert",
"Erik Schlögl"
] |
[
"Change Detection"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/joint-blind-motion-deblurring-and-depth
|
1711.10918
| null | null |
Joint Blind Motion Deblurring and Depth Estimation of Light Field
|
Removing camera motion blur from a single light field is a challenging task
since it is highly ill-posed inverse problem. The problem becomes even worse
when blur kernel varies spatially due to scene depth variation and high-order
camera motion. In this paper, we propose a novel algorithm to estimate all blur
model variables jointly, including latent sub-aperture image, camera motion,
and scene depth from the blurred 4D light field. Exploiting multi-view nature
of a light field relieves the inverse property of the optimization by utilizing
strong depth cues and multi-view blur observation. The proposed joint
estimation achieves high quality light field deblurring and depth estimation
simultaneously under arbitrary 6-DOF camera motion and unconstrained scene
depth. Intensive experiment on real and synthetic blurred light field confirms
that the proposed algorithm outperforms the state-of-the-art light field
deblurring and depth estimation methods.
| null |
http://arxiv.org/abs/1711.10918v2
|
http://arxiv.org/pdf/1711.10918v2.pdf
|
ECCV 2018 9
|
[
"Dongwoo Lee",
"Haesol Park",
"In Kyu Park",
"Kyoung Mu Lee"
] |
[
"Deblurring",
"Depth Estimation"
] | 2017-11-29T00:00:00 |
http://openaccess.thecvf.com/content_ECCV_2018/html/Dongwoo_Lee_Joint_Blind_Motion_ECCV_2018_paper.html
|
http://openaccess.thecvf.com/content_ECCV_2018/papers/Dongwoo_Lee_Joint_Blind_Motion_ECCV_2018_paper.pdf
|
joint-blind-motion-deblurring-and-depth-1
| null |
[] |
https://paperswithcode.com/paper/pcas-pruning-channels-with-attention
|
1806.05382
| null | null |
PCAS: Pruning Channels with Attention Statistics for Deep Network Compression
|
Compression techniques for deep neural networks are important for implementing them on small embedded devices. In particular, channel-pruning is a useful technique for realizing compact networks. However, many conventional methods require manual setting of compression ratios in each layer. It is difficult to analyze the relationships between all layers, especially for deeper models. To address these issues, we propose a simple channel-pruning technique based on attention statistics that enables to evaluate the importance of channels. We improved the method by means of a criterion for automatic channel selection, using a single compression ratio for the entire model in place of per-layer model analysis. The proposed approach achieved superior performance over conventional methods with respect to accuracy and the computational costs for various models and datasets. We provide analysis results for behavior of the proposed criterion on different datasets to demonstrate its favorable properties for channel pruning.
| null |
https://arxiv.org/abs/1806.05382v3
|
https://arxiv.org/pdf/1806.05382v3.pdf
| null |
[
"Kohei Yamamoto",
"Kurato Maeno"
] |
[
"channel selection"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/single-image-reflection-separation-with
|
1806.05376
| null | null |
Single Image Reflection Separation with Perceptual Losses
|
We present an approach to separating reflection from a single image. The
approach uses a fully convolutional network trained end-to-end with losses that
exploit low-level and high-level image information. Our loss function includes
two perceptual losses: a feature loss from a visual perception network, and an
adversarial loss that encodes characteristics of images in the transmission
layers. We also propose a novel exclusion loss that enforces pixel-level layer
separation. We create a dataset of real-world images with reflection and
corresponding ground-truth transmission layers for quantitative evaluation and
model training. We validate our method through comprehensive quantitative
experiments and show that our approach outperforms state-of-the-art reflection
removal methods in PSNR, SSIM, and perceptual user study. We also extend our
method to two other image enhancement tasks to demonstrate the generality of
our approach.
|
Our loss function includes two perceptual losses: a feature loss from a visual perception network, and an adversarial loss that encodes characteristics of images in the transmission layers.
|
http://arxiv.org/abs/1806.05376v1
|
http://arxiv.org/pdf/1806.05376v1.pdf
|
CVPR 2018 6
|
[
"Xuaner Zhang",
"Ren Ng",
"Qifeng Chen"
] |
[
"Image Enhancement",
"Reflection Removal",
"SSIM"
] | 2018-06-14T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Single_Image_Reflection_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Single_Image_Reflection_CVPR_2018_paper.pdf
|
single-image-reflection-separation-with-1
| null |
[] |
https://paperswithcode.com/paper/multi-attention-multi-class-constraint-for
|
1806.05372
| null | null |
Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition
|
Attention-based learning for fine-grained image recognition remains a
challenging task, where most of the existing methods treat each object part in
isolation, while neglecting the correlations among them. In addition, the
multi-stage or multi-scale mechanisms involved make the existing methods less
efficient and hard to be trained end-to-end. In this paper, we propose a novel
attention-based convolutional neural network (CNN) which regulates multiple
object parts among different input images. Our method first learns multiple
attention region features of each input image through the one-squeeze
multi-excitation (OSME) module, and then apply the multi-attention multi-class
constraint (MAMC) in a metric learning framework. For each anchor feature, the
MAMC functions by pulling same-attention same-class features closer, while
pushing different-attention or different-class features away. Our method can be
easily trained end-to-end, and is highly efficient which requires only one
training stage. Moreover, we introduce Dogs-in-the-Wild, a comprehensive dog
species dataset that surpasses similar existing datasets by category coverage,
data volume and annotation quality. This dataset will be released upon
acceptance to facilitate the research of fine-grained image recognition.
Extensive experiments are conducted to show the substantial improvements of our
method on four benchmark datasets.
|
Attention-based learning for fine-grained image recognition remains a challenging task, where most of the existing methods treat each object part in isolation, while neglecting the correlations among them.
|
http://arxiv.org/abs/1806.05372v1
|
http://arxiv.org/pdf/1806.05372v1.pdf
|
ECCV 2018 9
|
[
"Ming Sun",
"Yuchen Yuan",
"Feng Zhou",
"Errui Ding"
] |
[
"Fine-Grained Image Recognition",
"Metric Learning"
] | 2018-06-14T00:00:00 |
http://openaccess.thecvf.com/content_ECCV_2018/html/Ming_Sun_Multi-Attention_Multi-Class_Constraint_ECCV_2018_paper.html
|
http://openaccess.thecvf.com/content_ECCV_2018/papers/Ming_Sun_Multi-Attention_Multi-Class_Constraint_ECCV_2018_paper.pdf
|
multi-attention-multi-class-constraint-for-1
| null |
[] |
https://paperswithcode.com/paper/a-fast-proximal-point-method-for-computing
|
1802.04307
| null | null |
A Fast Proximal Point Method for Computing Exact Wasserstein Distance
|
Wasserstein distance plays increasingly important roles in machine learning, stochastic programming and image processing. Major efforts have been under way to address its high computational complexity, some leading to approximate or regularized variations such as Sinkhorn distance. However, as we will demonstrate, regularized variations with large regularization parameter will degradate the performance in several important machine learning applications, and small regularization parameter will fail due to numerical stability issues with existing algorithms. We address this challenge by developing an Inexact Proximal point method for exact Optimal Transport problem (IPOT) with the proximal operator approximately evaluated at each iteration using projections to the probability simplex. The algorithm (a) converges to exact Wasserstein distance with theoretical guarantee and robust regularization parameter selection, (b) alleviates numerical stability issue, (c) has similar computational complexity to Sinkhorn, and (d) avoids the shrinking problem when apply to generative models. Furthermore, a new algorithm is proposed based on IPOT to obtain sharper Wasserstein barycenter.
|
However, as we will demonstrate, regularized variations with large regularization parameter will degradate the performance in several important machine learning applications, and small regularization parameter will fail due to numerical stability issues with existing algorithms.
|
https://arxiv.org/abs/1802.04307v3
|
https://arxiv.org/pdf/1802.04307v3.pdf
| null |
[
"Yujia Xie",
"Xiangfeng Wang",
"Ruijia Wang",
"Hongyuan Zha"
] |
[
"BIG-bench Machine Learning"
] | 2018-02-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/fire-ssd-wide-fire-modules-based-single-shot
|
1806.05363
| null | null |
Fire SSD: Wide Fire Modules based Single Shot Detector on Edge Device
|
With the emergence of edge computing, there is an increasing need for running
convolutional neural network based object detection on small form factor edge
computing devices with limited compute and thermal budget for applications such
as video surveillance. To address this problem, efficient object detection
frameworks such as YOLO and SSD were proposed. However, SSD based object
detection that uses VGG16 as backend network is insufficient to achieve real
time speed on edge devices. To further improve the detection speed, the backend
network is replaced by more efficient networks such as SqueezeNet and
MobileNet. Although the speed is greatly improved, it comes with a price of
lower accuracy. In this paper, we propose an efficient SSD named Fire SSD. Fire
SSD achieves 70.7mAP on Pascal VOC 2007 test set. Fire SSD achieves the speed
of 30.6FPS on low power mainstream CPU and is about 6 times faster than SSD300
and has about 4 times smaller model size. Fire SSD also achieves 22.2FPS on
integrated GPU.
| null |
http://arxiv.org/abs/1806.05363v5
|
http://arxiv.org/pdf/1806.05363v5.pdf
| null |
[
"Hengfui Liau",
"Nimmagadda Yamini",
"YengLiong Wong"
] |
[
"CPU",
"Edge-computing",
"GPU",
"Object",
"object-detection",
"Object Detection"
] | 2018-06-14T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Average Pooling** is a pooling operation that calculates the average value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs. It extracts features more smoothly than [Max Pooling](https://paperswithcode.com/method/max-pooling), whereas max pooling extracts more pronounced features like edges.\r\n\r\nImage Source: [here](https://www.researchgate.net/figure/Illustration-of-Max-Pooling-and-Average-Pooling-Figure-2-above-shows-an-example-of-max_fig2_333593451)",
"full_name": "Average Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Average Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/6db1569c89094cf23f3bc41f79275c45e9fcb3f3/torchvision/models/squeezenet.py#L14",
"description": "A **Fire Module** is a building block for convolutional neural networks, notably used as part of [SqueezeNet](https://paperswithcode.com/method/squeezenet). A Fire module is comprised of: a squeeze [convolution](https://paperswithcode.com/method/convolution) layer (which has only 1x1 filters), feeding into an expand layer that has a mix of 1x1 and 3x3 convolution filters. We expose three tunable dimensions (hyperparameters) in a Fire module: $s\\_{1x1}$, $e\\_{1x1}$, and $e\\_{3x3}$. In a Fire module, $s\\_{1x1}$ is the number of filters in the squeeze layer (all 1x1), $e\\_{1x1}$ is the number of 1x1 filters in the expand layer, and $e\\_{3x3}$ is the number of 3x3 filters in the expand layer. When we use Fire modules we set $s\\_{1x1}$ to be less than ($e\\_{1x1}$ + $e\\_{3x3}$), so the squeeze layer helps to limit the number of input channels to the 3x3 filters.",
"full_name": "Fire Module",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Model Blocks** are building blocks used in image models such as convolutional neural networks. Below you can find a continuously updating list of image model blocks.",
"name": "Image Model Blocks",
"parent": null
},
"name": "Fire Module",
"source_title": "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size",
"source_url": "http://arxiv.org/abs/1602.07360v4"
},
{
"code_snippet_url": null,
"description": "**Non Maximum Suppression** is a computer vision method that selects a single entity out of many overlapping entities (for example bounding boxes in object detection). The criteria is usually discarding entities that are below a given probability bound. With remaining entities we repeatedly pick the entity with the highest probability, output that as the prediction, and discard any remaining box where a $\\text{IoU} \\geq 0.5$ with the box output in the previous step.\r\n\r\nImage Credit: [Martin Kersner](https://github.com/martinkersner/non-maximum-suppression-cpp)",
"full_name": "Non Maximum Suppression",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Proposal Filtering",
"parent": null
},
"name": "Non Maximum Suppression",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/baa592b215804927e28638f6a7f3318cbc411d49/torchvision/models/resnet.py#L157",
"description": "**Global Average Pooling** is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map for each corresponding category of the classification task in the last mlpconv layer. Instead of adding fully connected layers on top of the feature maps, we take the average of each feature map, and the resulting vector is fed directly into the [softmax](https://paperswithcode.com/method/softmax) layer. \r\n\r\nOne advantage of global [average pooling](https://paperswithcode.com/method/average-pooling) over the fully connected layers is that it is more native to the [convolution](https://paperswithcode.com/method/convolution) structure by enforcing correspondences between feature maps and categories. Thus the feature maps can be easily interpreted as categories confidence maps. Another advantage is that there is no parameter to optimize in the global average pooling thus overfitting is avoided at this layer. Furthermore, global average pooling sums out the spatial information, thus it is more robust to spatial translations of the input.",
"full_name": "Global Average Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Global Average Pooling",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"code_snippet_url": "",
"description": "A **1 x 1 Convolution** is a [convolution](https://paperswithcode.com/method/convolution) with some special properties in that it can be used for dimensionality reduction, efficient low dimensional embeddings, and applying non-linearity after convolutions. It maps an input pixel with all its channels to an output pixel which can be squeezed to a desired output depth. It can be viewed as an [MLP](https://paperswithcode.com/method/feedforward-network) looking at a particular pixel location.\r\n\r\nImage Credit: [http://deeplearning.ai](http://deeplearning.ai)",
"full_name": "1x1 Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "1x1 Convolution",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/0adb5843766092fba584791af76383125fd0d01c/torch/nn/init.py#L289",
"description": "**Xavier Initialization**, or **Glorot Initialization**, is an initialization scheme for neural networks. Biases are initialized be 0 and the weights $W\\_{ij}$ at each layer are initialized as:\r\n\r\n$$ W\\_{ij} \\sim U\\left[-\\frac{\\sqrt{6}}{\\sqrt{fan_{in} + fan_{out}}}, \\frac{\\sqrt{6}}{\\sqrt{fan_{in} + fan_{out}}}\\right] $$\r\n\r\nWhere $U$ is a uniform distribution and $fan_{in}$ is the size of the previous layer (number of columns in $W$) and $fan_{out}$ is the size of the current layer.",
"full_name": "Xavier Initialization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Initialization** methods are used to initialize the weights in a neural network. Below can you find a continuously updating list of initialization methods.",
"name": "Initialization",
"parent": null
},
"name": "Xavier Initialization",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Max Pooling** is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs.\r\n\r\nImage Source: [here](https://computersciencewiki.org/index.php/File:MaxpoolSample2.png)",
"full_name": "Max Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Max Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/amdegroot/ssd.pytorch/blob/5b0b77faa955c1917b0c710d770739ba8fbff9b7/ssd.py#L10",
"description": "**SSD** is a single-stage object detection method that discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. \r\n\r\nThe fundamental improvement in speed comes from eliminating bounding box proposals and the subsequent pixel or feature resampling stage. Improvements over competing single-stage methods include using a small convolutional filter to predict object categories and offsets in bounding box locations, using separate predictors (filters) for different aspect ratio detections, and applying these filters to multiple feature maps from the later stages of a network in order to perform detection at multiple scales.",
"full_name": "SSD",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Object Detection Models** are architectures used to perform the task of object detection. Below you can find a continuously updating list of object detection models.",
"name": "Object Detection Models",
"parent": null
},
"name": "SSD",
"source_title": "SSD: Single Shot MultiBox Detector",
"source_url": "http://arxiv.org/abs/1512.02325v5"
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/6db1569c89094cf23f3bc41f79275c45e9fcb3f3/torchvision/models/squeezenet.py#L37",
"description": "**SqueezeNet** is a convolutional neural network that employs design strategies to reduce the number of parameters, notably with the use of fire modules that \"squeeze\" parameters using 1x1 convolutions.",
"full_name": "SqueezeNet",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "If you have questions or want to make special travel arrangements, you can make them online or call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. For hearing or speech impaired assistance dial 711 to be connected through the National Relay Service.",
"name": "Convolutional Neural Networks",
"parent": "Image Models"
},
"name": "SqueezeNet",
"source_title": "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size",
"source_url": "http://arxiv.org/abs/1602.07360v4"
}
] |
https://paperswithcode.com/paper/adagrad-stepsizes-sharp-convergence-over
|
1806.01811
| null | null |
AdaGrad stepsizes: Sharp convergence over nonconvex landscapes
|
Adaptive gradient methods such as AdaGrad and its variants update the stepsize in stochastic gradient descent on the fly according to the gradients received along the way; such methods have gained widespread use in large-scale optimization for their ability to converge robustly, without the need to fine-tune the stepsize schedule. Yet, the theoretical guarantees to date for AdaGrad are for online and convex optimization. We bridge this gap by providing theoretical guarantees for the convergence of AdaGrad for smooth, nonconvex functions. We show that the norm version of AdaGrad (AdaGrad-Norm) converges to a stationary point at the $\mathcal{O}(\log(N)/\sqrt{N})$ rate in the stochastic setting, and at the optimal $\mathcal{O}(1/N)$ rate in the batch (non-stochastic) setting -- in this sense, our convergence guarantees are 'sharp'. In particular, the convergence of AdaGrad-Norm is robust to the choice of all hyper-parameters of the algorithm, in contrast to stochastic gradient descent whose convergence depends crucially on tuning the step-size to the (generally unknown) Lipschitz smoothness constant and level of stochastic noise on the gradient. Extensive numerical experiments are provided to corroborate our theory; moreover, the experiments suggest that the robustness of AdaGrad-Norm extends to state-of-the-art models in deep learning, without sacrificing generalization.
|
Adaptive gradient methods such as AdaGrad and its variants update the stepsize in stochastic gradient descent on the fly according to the gradients received along the way; such methods have gained widespread use in large-scale optimization for their ability to converge robustly, without the need to fine-tune the stepsize schedule.
|
https://arxiv.org/abs/1806.01811v8
|
https://arxiv.org/pdf/1806.01811v8.pdf
| null |
[
"Rachel Ward",
"Xiaoxia Wu",
"Leon Bottou"
] |
[
"Stochastic Optimization"
] | 2018-06-05T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/Dawn-Of-Eve/nadir/blob/main/src/nadir/adagrad.py",
"description": "**AdaGrad** is a stochastic optimization method that adapts the learning rate to the parameters. It performs smaller updates for parameters associated with frequently occurring features, and larger updates for parameters associated with infrequently occurring features. In its update rule, Adagrad modifies the general learning rate $\\eta$ at each time step $t$ for every parameter $\\theta\\_{i}$ based on the past gradients for $\\theta\\_{i}$: \r\n\r\n$$ \\theta\\_{t+1, i} = \\theta\\_{t, i} - \\frac{\\eta}{\\sqrt{G\\_{t, ii} + \\epsilon}}g\\_{t, i} $$\r\n\r\nThe benefit of AdaGrad is that it eliminates the need to manually tune the learning rate; most leave it at a default value of $0.01$. Its main weakness is the accumulation of the squared gradients in the denominator. Since every added term is positive, the accumulated sum keeps growing during training, causing the learning rate to shrink and becoming infinitesimally small.\r\n\r\nImage: [Alec Radford](https://twitter.com/alecrad)",
"full_name": "AdaGrad",
"introduced_year": 2011,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "AdaGrad",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/view-volume-network-for-semantic-scene
|
1806.05361
| null | null |
View-volume Network for Semantic Scene Completion from a Single Depth Image
|
We introduce a View-Volume convolutional neural network (VVNet) for inferring
the occupancy and semantic labels of a volumetric 3D scene from a single depth
image. The VVNet concatenates a 2D view CNN and a 3D volume CNN with a
differentiable projection layer. Given a single RGBD image, our method extracts
the detailed geometric features from the input depth image with a 2D view CNN
and then projects the features into a 3D volume according to the input depth
map via a projection layer. After that, we learn the 3D context information of
the scene with a 3D volume CNN for computing the result volumetric occupancy
and semantic labels. With combined 2D and 3D representations, the VVNet
efficiently reduces the computational cost, enables feature extraction from
multi-channel high resolution inputs, and thus significantly improves the
result accuracy. We validate our method and demonstrate its efficiency and
effectiveness on both synthetic SUNCG and real NYU dataset.
| null |
http://arxiv.org/abs/1806.05361v1
|
http://arxiv.org/pdf/1806.05361v1.pdf
| null |
[
"Yu-Xiao Guo",
"Xin Tong"
] |
[
"3D Semantic Scene Completion"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/defending-against-saddle-point-attack-in
|
1806.05358
| null | null |
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning
|
We study robust distributed learning that involves minimizing a non-convex loss function with saddle points. We consider the Byzantine setting where some worker machines have abnormal or even arbitrary and adversarial behavior. In this setting, the Byzantine machines may create fake local minima near a saddle point that is far away from any true local minimum, even when robust gradient estimators are used. We develop ByzantinePGD, a robust first-order algorithm that can provably escape saddle points and fake local minima, and converge to an approximate true local minimizer with low iteration complexity. As a by-product, we give a simpler algorithm and analysis for escaping saddle points in the usual non-Byzantine setting. We further discuss three robust gradient estimators that can be used in ByzantinePGD, including median, trimmed mean, and iterative filtering. We characterize their performance in concrete statistical settings, and argue for their near-optimality in low and high dimensional regimes.
| null |
https://arxiv.org/abs/1806.05358v4
|
https://arxiv.org/pdf/1806.05358v4.pdf
| null |
[
"Dong Yin",
"Yudong Chen",
"Kannan Ramchandran",
"Peter Bartlett"
] |
[] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-multi-output-forecasting-learning-to
|
1806.05357
| null | null |
Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories
|
In many forecasting applications, it is valuable to predict not only the
value of a signal at a certain time point in the future, but also the values
leading up to that point. This is especially true in clinical applications,
where the future state of the patient can be less important than the patient's
overall trajectory. This requires multi-step forecasting, a forecasting variant
where one aims to predict multiple values in the future simultaneously.
Standard methods to accomplish this can propagate error from prediction to
prediction, reducing quality over the long term. In light of these challenges,
we propose multi-output deep architectures for multi-step forecasting in which
we explicitly model the distribution of future values of the signal over a
prediction horizon. We apply these techniques to the challenging and clinically
relevant task of blood glucose forecasting. Through a series of experiments on
a real-world dataset consisting of 550K blood glucose measurements, we
demonstrate the effectiveness of our proposed approaches in capturing the
underlying signal dynamics. Compared to existing shallow and deep methods, we
find that our proposed approaches improve performance individually and capture
complementary information, leading to a large improvement over the baseline
when combined (4.87 vs. 5.31 absolute percentage error (APE)). Overall, the
results suggest the efficacy of our proposed approach in predicting blood
glucose level and multi-step forecasting more generally.
|
Overall, the results suggest the efficacy of our proposed approach in predicting blood glucose level and multi-step forecasting more generally.
|
http://arxiv.org/abs/1806.05357v1
|
http://arxiv.org/pdf/1806.05357v1.pdf
| null |
[
"Ian Fox",
"Lynn Ang",
"Mamta Jaiswal",
"Rodica Pop-Busui",
"Jenna Wiens"
] |
[] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/finding-gems-multi-scale-dictionaries-for
|
1806.05356
| null | null |
Finding GEMS: Multi-Scale Dictionaries for High-Dimensional Graph Signals
|
Modern data introduces new challenges to classic signal processing
approaches, leading to a growing interest in the field of graph signal
processing. A powerful and well established model for real world signals in
various domains is sparse representation over a dictionary, combined with the
ability to train the dictionary from signal examples. This model has been
successfully applied to graph signals as well by integrating the underlying
graph topology into the learned dictionary. Nonetheless, dictionary learning
methods for graph signals are typically restricted to small dimensions due to
the computational constraints that the dictionary learning problem entails, and
due to the direct use of the graph Laplacian matrix. In this paper, we propose
a dictionary learning algorithm that applies to a broader class of graph
signals, and is capable of handling much higher dimensional data. We
incorporate the underlying graph topology both implicitly, by forcing the
learned dictionary atoms to be sparse combinations of graph-wavelet functions,
and explicitly, by adding direct graph constraints to promote smoothness in
both the feature and manifold domains. The resulting atoms are thus adapted to
the data of interest while adhering to the underlying graph structure and
possessing a desired multi-scale property. Experimental results on several
datasets, representing both synthetic and real network data of different
nature, demonstrate the effectiveness of the proposed algorithm for graph
signal processing even in high dimensions.
| null |
http://arxiv.org/abs/1806.05356v1
|
http://arxiv.org/pdf/1806.05356v1.pdf
| null |
[
"Yael Yankelevsky",
"Michael Elad"
] |
[
"Dictionary Learning",
"Vocal Bursts Intensity Prediction"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/scalable-neural-network-compression-and
|
1806.05355
| null | null |
Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization
|
We propose a simple and easy to implement neural network compression
algorithm that achieves results competitive with more complicated
state-of-the-art methods. The key idea is to modify the original optimization
problem by adding K independent Gaussian priors (corresponding to the k-means
objective) over the network parameters to achieve parameter quantization, as
well as an L1 penalty to achieve pruning. Unlike many existing
quantization-based methods, our method uses hard clustering assignments of
network parameters, which adds minimal change or overhead to standard network
training. We also demonstrate experimentally that tying neural network
parameters provides less gain in generalization performance than changing
network architecture and connectivity patterns entirely.
| null |
http://arxiv.org/abs/1806.05355v1
|
http://arxiv.org/pdf/1806.05355v1.pdf
| null |
[
"Yibo Yang",
"Nicholas Ruozzi",
"Vibhav Gogate"
] |
[
"Clustering",
"Neural Network Compression",
"Quantization"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/q-neurons-neuron-activations-based-on
|
1806.00149
| null |
r1xkIjA9tX
|
q-Neurons: Neuron Activations based on Stochastic Jackson's Derivative Operators
|
We propose a new generic type of stochastic neurons, called $q$-neurons, that
considers activation functions based on Jackson's $q$-derivatives with
stochastic parameters $q$. Our generalization of neural network architectures
with $q$-neurons is shown to be both scalable and very easy to implement. We
demonstrate experimentally consistently improved performances over
state-of-the-art standard activation functions, both on training and testing
loss functions.
|
We propose a new generic type of stochastic neurons, called $q$-neurons, that considers activation functions based on Jackson's $q$-derivatives with stochastic parameters $q$.
|
http://arxiv.org/abs/1806.00149v2
|
http://arxiv.org/pdf/1806.00149v2.pdf
| null |
[
"Frank Nielsen",
"Ke Sun"
] |
[] | 2018-06-01T00:00:00 |
https://openreview.net/forum?id=r1xkIjA9tX
|
https://openreview.net/pdf?id=r1xkIjA9tX
| null | null |
[] |
https://paperswithcode.com/paper/learning-to-explain-an-information-theoretic
|
1802.07814
| null | null |
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
|
We introduce instancewise feature selection as a methodology for model
interpretation. Our method is based on learning a function to extract a subset
of features that are most informative for each given example. This feature
selector is trained to maximize the mutual information between selected
features and the response variable, where the conditional distribution of the
response variable given the input is the model to be explained. We develop an
efficient variational approximation to the mutual information, and show the
effectiveness of our method on a variety of synthetic and real data sets using
both quantitative metrics and human evaluation.
|
We introduce instancewise feature selection as a methodology for model interpretation.
|
http://arxiv.org/abs/1802.07814v2
|
http://arxiv.org/pdf/1802.07814v2.pdf
|
ICML 2018 7
|
[
"Jianbo Chen",
"Le Song",
"Martin J. Wainwright",
"Michael. I. Jordan"
] |
[
"feature selection"
] | 2018-02-21T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=1957
|
http://proceedings.mlr.press/v80/chen18j/chen18j.pdf
|
learning-to-explain-an-information-theoretic-1
| null |
[] |
https://paperswithcode.com/paper/stingray-detection-of-aerial-images-using
|
1805.04262
| null | null |
Stingray Detection of Aerial Images Using Augmented Training Images Generated by A Conditional Generative Model
|
In this paper, we present an object detection method that tackles the
stingray detection problem based on aerial images. In this problem, the images
are aerially captured on a sea-surface area by using an Unmanned Aerial Vehicle
(UAV), and the stingrays swimming under (but close to) the sea surface are the
target we want to detect and locate. To this end, we use a deep object
detection method, faster RCNN, to train a stingray detector based on a limited
training set of images. To boost the performance, we develop a new generative
approach, conditional GLO, to increase the training samples of stingray, which
is an extension of the Generative Latent Optimization (GLO) approach. Unlike
traditional data augmentation methods that generate new data only for image
classification, our proposed method that mixes foreground and background
together can generate new data for an object detection task, and thus improve
the training efficacy of a CNN detector. Experimental results show that
satisfiable performance can be obtained by using our approach on stingray
detection in aerial images.
| null |
http://arxiv.org/abs/1805.04262v3
|
http://arxiv.org/pdf/1805.04262v3.pdf
| null |
[
"Yi-Min Chou",
"Chien-Hung Chen",
"Keng-Hao Liu",
"Chu-Song Chen"
] |
[
"Data Augmentation",
"image-classification",
"Image Classification",
"Object",
"object-detection",
"Object Detection"
] | 2018-05-11T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/convex-class-model-on-symmetric-positive
|
1806.05343
| null | null |
Convex Class Model on Symmetric Positive Definite Manifolds
|
The effectiveness of Symmetric Positive Definite (SPD) manifold features has been proven in various computer vision tasks. However, due to the non-Euclidean geometry of these features, existing Euclidean machineries cannot be directly used. In this paper, we tackle the classification tasks with limited training data on SPD manifolds. Our proposed framework, named Manifold Convex Class Model, represents each class on SPD manifolds using a convex model, and classification can be performed by computing distances to the convex models. We provide three methods based on different metrics to address the optimization problem of the smallest distance of a point to the convex model on SPD manifold. The efficacy of our proposed framework is demonstrated both on synthetic data and several computer vision tasks including object recognition, texture classification, person re-identification and traffic scene classification.
| null |
https://arxiv.org/abs/1806.05343v2
|
https://arxiv.org/pdf/1806.05343v2.pdf
| null |
[
"Kun Zhao",
"Arnold Wiliem",
"Shaokang Chen",
"Brian C. Lovell"
] |
[
"Classification",
"General Classification",
"model",
"Object Recognition",
"Person Re-Identification",
"Scene Classification",
"Texture Classification"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/context-aware-policy-reuse
|
1806.03793
| null | null |
Context-Aware Policy Reuse
|
Transfer learning can greatly speed up reinforcement learning for a new task
by leveraging policies of relevant tasks.
Existing works of policy reuse either focus on only selecting a single best
source policy for transfer without considering contexts, or cannot guarantee to
learn an optimal policy for a target task.
To improve transfer efficiency and guarantee optimality, we develop a novel
policy reuse method, called Context-Aware Policy reuSe (CAPS), that enables
multi-policy transfer. Our method learns when and which source policy is best
for reuse, as well as when to terminate its reuse. CAPS provides theoretical
guarantees in convergence and optimality for both source policy selection and
target task learning. Empirical results on a grid-based navigation domain and
the Pygame Learning Environment demonstrate that CAPS significantly outperforms
other state-of-the-art policy reuse methods.
| null |
http://arxiv.org/abs/1806.03793v4
|
http://arxiv.org/pdf/1806.03793v4.pdf
| null |
[
"Siyuan Li",
"Fangda Gu",
"Guangxiang Zhu",
"Chongjie Zhang"
] |
[
"Reinforcement Learning",
"Transfer Learning"
] | 2018-06-11T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/from-trailers-to-storylines-an-efficient-way
|
1806.05341
| null | null |
From Trailers to Storylines: An Efficient Way to Learn from Movies
|
The millions of movies produced in the human history are valuable resources
for computer vision research. However, learning a vision model from movie data
would meet with serious difficulties. A major obstacle is the computational
cost -- the length of a movie is often over one hour, which is substantially
longer than the short video clips that previous study mostly focuses on. In
this paper, we explore an alternative approach to learning vision models from
movies. Specifically, we consider a framework comprised of a visual module and
a temporal analysis module. Unlike conventional learning methods, the proposed
approach learns these modules from different sets of data -- the former from
trailers while the latter from movies. This allows distinctive visual features
to be learned within a reasonable budget while still preserving long-term
temporal structures across an entire movie. We construct a large-scale dataset
for this study and define a series of tasks on top. Experiments on this dataset
showed that the proposed method can substantially reduce the training time
while obtaining highly effective features and coherent temporal structures.
|
Experiments on this dataset showed that the proposed method can substantially reduce the training time while obtaining highly effective features and coherent temporal structures.
|
http://arxiv.org/abs/1806.05341v1
|
http://arxiv.org/pdf/1806.05341v1.pdf
| null |
[
"Qingqiu Huang",
"Yuanjun Xiong",
"Yu Xiong",
"Yuqi Zhang",
"Dahua Lin"
] |
[] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/hierarchical-interpretations-for-neural
|
1806.05337
| null |
SkEqro0ctQ
|
Hierarchical interpretations for neural network predictions
|
Deep neural networks (DNNs) have achieved impressive predictive performance
due to their ability to learn complex, non-linear relationships between
variables. However, the inability to effectively visualize these relationships
has led to DNNs being characterized as black boxes and consequently limited
their applications. To ameliorate this problem, we introduce the use of
hierarchical interpretations to explain DNN predictions through our proposed
method, agglomerative contextual decomposition (ACD). Given a prediction from a
trained DNN, ACD produces a hierarchical clustering of the input features,
along with the contribution of each cluster to the final prediction. This
hierarchy is optimized to identify clusters of features that the DNN learned
are predictive. Using examples from Stanford Sentiment Treebank and ImageNet,
we show that ACD is effective at diagnosing incorrect predictions and
identifying dataset bias. Through human experiments, we demonstrate that ACD
enables users both to identify the more accurate of two DNNs and to better
trust a DNN's outputs. We also find that ACD's hierarchy is largely robust to
adversarial perturbations, implying that it captures fundamental aspects of the
input and ignores spurious noise.
|
Deep neural networks (DNNs) have achieved impressive predictive performance due to their ability to learn complex, non-linear relationships between variables.
|
http://arxiv.org/abs/1806.05337v2
|
http://arxiv.org/pdf/1806.05337v2.pdf
|
ICLR 2019 5
|
[
"Chandan Singh",
"W. James Murdoch",
"Bin Yu"
] |
[
"Clustering",
"Feature Importance",
"Interpretable Machine Learning"
] | 2018-06-14T00:00:00 |
https://openreview.net/forum?id=SkEqro0ctQ
|
https://openreview.net/pdf?id=SkEqro0ctQ
|
hierarchical-interpretations-for-neural-1
| null |
[
{
"code_snippet_url": "https://github.com/csinva/hierarchical-dnn-interpretations",
"description": "**Agglomerative Contextual Decomposition (ACD)** is an interpretability method that produces hierarchical interpretations for a single prediction made by a neural network, by scoring interactions and building them into a tree. Given a prediction from a trained neural network, ACD produces a hierarchical clustering of the input features, along with the contribution of each cluster to the final prediction. This hierarchy is optimized to identify clusters of features that the DNN learned are predictive.",
"full_name": "Agglomerative Contextual Decomposition",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Interpretability Methods** seek to explain the predictions made by neural networks by introducing mechanisms to enduce or enforce interpretability. For example, LIME approximates the neural network with a locally interpretable model. Below you can find a continuously updating list of interpretability methods.",
"name": "Interpretability",
"parent": null
},
"name": "Agglomerative Contextual Decomposition",
"source_title": "Hierarchical interpretations for neural network predictions",
"source_url": "http://arxiv.org/abs/1806.05337v2"
}
] |
https://paperswithcode.com/paper/adversarial-learning-with-local-coordinate
|
1806.04895
| null | null |
Adversarial Learning with Local Coordinate Coding
|
Generative adversarial networks (GANs) aim to generate realistic data from
some prior distribution (e.g., Gaussian noises). However, such prior
distribution is often independent of real data and thus may lose semantic
information (e.g., geometric structure or content in images) of data. In
practice, the semantic information might be represented by some latent
distribution learned from data, which, however, is hard to be used for sampling
in GANs. In this paper, rather than sampling from the pre-defined prior
distribution, we propose a Local Coordinate Coding (LCC) based sampling method
to improve GANs. We derive a generalization bound for LCC based GANs and prove
that a small dimensional input is sufficient to achieve good generalization.
Extensive experiments on various real-world datasets demonstrate the
effectiveness of the proposed method.
| null |
http://arxiv.org/abs/1806.04895v2
|
http://arxiv.org/pdf/1806.04895v2.pdf
|
ICML 2018 7
|
[
"Jiezhang Cao",
"Yong Guo",
"Qingyao Wu",
"Chunhua Shen",
"Junzhou Huang",
"Mingkui Tan"
] |
[] | 2018-06-13T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=1902
|
http://proceedings.mlr.press/v80/cao18a/cao18a.pdf
|
adversarial-learning-with-local-coordinate-1
| null |
[
{
"code_snippet_url": null,
"description": "Please enter a description about the method here",
"full_name": "Lipschitz Constant Constraint",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "LCC",
"source_title": "Regularisation of Neural Networks by Enforcing Lipschitz Continuity",
"source_url": "https://arxiv.org/abs/1804.04368v3"
}
] |
https://paperswithcode.com/paper/talakat-bullet-hell-generation-through
|
1806.04718
| null | null |
Talakat: Bullet Hell Generation through Constrained Map-Elites
|
We describe a search-based approach to generating new levels for bullet hell
games, which are action games characterized by and requiring avoidance of a
very large amount of projectiles. Levels are represented using a
domain-specific description language, and search in the space defined by this
language is performed by a novel variant of the Map-Elites algorithm which
incorporates a feasible- infeasible approach to constraint satisfaction.
Simulation-based evaluation is used to gauge the fitness of levels, using an
agent based on best-first search. The performance of the agent can be tuned
according to the two dimensions of strategy and dexterity, making it possible
to search for level configurations that require a specific combination of both.
As far as we know, this paper describes the first generator for this game
genre, and includes several algorithmic innovations.
|
We describe a search-based approach to generating new levels for bullet hell games, which are action games characterized by and requiring avoidance of a very large amount of projectiles.
|
http://arxiv.org/abs/1806.04718v2
|
http://arxiv.org/pdf/1806.04718v2.pdf
| null |
[
"Ahmed Khalifa",
"Scott Lee",
"Andy Nealen",
"Julian Togelius"
] |
[] | 2018-06-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-generative-modeling-approach-to-limited
|
1802.06458
| null | null |
A Generative Modeling Approach to Limited Channel ECG Classification
|
Processing temporal sequences is central to a variety of applications in
health care, and in particular multi-channel Electrocardiogram (ECG) is a
highly prevalent diagnostic modality that relies on robust sequence modeling.
While Recurrent Neural Networks (RNNs) have led to significant advances in
automated diagnosis with time-series data, they perform poorly when models are
trained using a limited set of channels. A crucial limitation of existing
solutions is that they rely solely on discriminative models, which tend to
generalize poorly in such scenarios. In order to combat this limitation, we
develop a generative modeling approach to limited channel ECG classification.
This approach first uses a Seq2Seq model to implicitly generate the missing
channel information, and then uses the latent representation to perform the
actual supervisory task. This decoupling enables the use of unsupervised data
and also provides highly robust metric spaces for subsequent discriminative
learning. Our experiments with the Physionet dataset clearly evidence the
effectiveness of our approach over standard RNNs in disease prediction.
| null |
http://arxiv.org/abs/1802.06458v3
|
http://arxiv.org/pdf/1802.06458v3.pdf
| null |
[
"Deepta Rajan",
"Jayaraman J. Thiagarajan"
] |
[
"Classification",
"Diagnostic",
"Disease Prediction",
"ECG Classification",
"General Classification",
"Temporal Sequences",
"Time Series",
"Time Series Analysis"
] | 2018-02-18T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\\right)}$$\r\n\r\nSome drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.",
"full_name": "Sigmoid Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Sigmoid Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nHistorically, the tanh function became preferred over the [sigmoid function](https://paperswithcode.com/method/sigmoid-activation) as it gave better performance for multi-layer neural networks. But it did not solve the vanishing gradient problem that sigmoids suffered, which was tackled more effectively with the introduction of [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nImage Source: [Junxi Feng](https://www.researchgate.net/profile/Junxi_Feng)",
"full_name": "Tanh Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Tanh Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.\r\n\r\n(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))\r\n\r\n(Introduced by Hochreiter and Schmidhuber)",
"full_name": "Long Short-Term Memory",
"introduced_year": 1997,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "LSTM",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Seq2Seq**, or **Sequence To Sequence**, is a model used in sequence prediction tasks, such as language modelling and machine translation. The idea is to use one [LSTM](https://paperswithcode.com/method/lstm), the *encoder*, to read the input sequence one timestep at a time, to obtain a large fixed dimensional vector representation (a context vector), and then to use another LSTM, the *decoder*, to extract the output sequence\r\nfrom that vector. The second LSTM is essentially a recurrent neural network language model except that it is conditioned on the input sequence.\r\n\r\n(Note that this page refers to the original seq2seq not general sequence-to-sequence models)",
"full_name": "Sequence to Sequence",
"introduced_year": 2000,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Sequence To Sequence Models",
"parent": null
},
"name": "Seq2Seq",
"source_title": "Sequence to Sequence Learning with Neural Networks",
"source_url": "http://arxiv.org/abs/1409.3215v3"
}
] |
https://paperswithcode.com/paper/scsp-spectral-clustering-filter-pruning-with
|
1806.05320
| null | null |
SCSP: Spectral Clustering Filter Pruning with Soft Self-adaption Manners
|
Deep Convolutional Neural Networks (CNN) has achieved significant success in
computer vision field. However, the high computational cost of the deep complex
models prevents the deployment on edge devices with limited memory and
computational resource. In this paper, we proposed a novel filter pruning for
convolutional neural networks compression, namely spectral clustering filter
pruning with soft self-adaption manners (SCSP). We first apply spectral
clustering on filters layer by layer to explore their intrinsic connections and
only count on efficient groups. By self-adaption manners, the pruning
operations can be done in few epochs to let the network gradually choose
meaningful groups. According to this strategy, we not only achieve model
compression while keeping considerable performance, but also find a novel angle
to interpret the model compression process.
| null |
http://arxiv.org/abs/1806.05320v1
|
http://arxiv.org/pdf/1806.05320v1.pdf
| null |
[
"Huiyuan Zhuo",
"Xuelin Qian",
"Yanwei Fu",
"Heng Yang",
"xiangyang xue"
] |
[
"Clustering",
"Model Compression"
] | 2018-06-14T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Pruning",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Model Compression",
"parent": null
},
"name": "Pruning",
"source_title": "Pruning Filters for Efficient ConvNets",
"source_url": "http://arxiv.org/abs/1608.08710v3"
},
{
"code_snippet_url": "",
"description": "Spectral clustering has attracted increasing attention due to\r\nthe promising ability in dealing with nonlinearly separable datasets [15], [16]. In spectral clustering, the spectrum of the graph Laplacian is used to reveal the cluster structure. The spectral clustering algorithm mainly consists of two steps: 1) constructs the low dimensional embedded representation of the data based on the eigenvectors of the graph Laplacian, 2) applies k-means on the constructed low dimensional data to obtain the clustering result. Thus,",
"full_name": "Spectral Clustering",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Clustering** methods cluster a dataset so that similar datapoints are located in the same group. Below you can find a continuously updating list of clustering methods.",
"name": "Clustering",
"parent": null
},
"name": "Spectral Clustering",
"source_title": "A Tutorial on Spectral Clustering",
"source_url": "http://arxiv.org/abs/0711.0189v1"
}
] |
https://paperswithcode.com/paper/interpretable-partitioned-embedding-for
|
1806.04845
| null | null |
Interpretable Partitioned Embedding for Customized Fashion Outfit Composition
|
Intelligent fashion outfit composition becomes more and more popular in these
years. Some deep learning based approaches reveal competitive composition
recently. However, the unexplainable characteristic makes such deep learning
based approach cannot meet the the designer, businesses and consumers' urge to
comprehend the importance of different attributes in an outfit composition. To
realize interpretable and customized fashion outfit compositions, we propose a
partitioned embedding network to learn interpretable representations from
clothing items. The overall network architecture consists of three components:
an auto-encoder module, a supervised attributes module and a multi-independent
module. The auto-encoder module serves to encode all useful information into
the embedding. In the supervised attributes module, multiple attributes labels
are adopted to ensure that different parts of the overall embedding correspond
to different attributes. In the multi-independent module, adversarial operation
are adopted to fulfill the mutually independent constraint. With the
interpretable and partitioned embedding, we then construct an outfit
composition graph and an attribute matching map. Given specified attributes
description, our model can recommend a ranked list of outfit composition with
interpretable matching scores. Extensive experiments demonstrate that 1) the
partitioned embedding have unmingled parts which corresponding to different
attributes and 2) outfits recommended by our model are more desirable in
comparison with the existing methods.
| null |
http://arxiv.org/abs/1806.04845v4
|
http://arxiv.org/pdf/1806.04845v4.pdf
| null |
[
"Zunlei Feng",
"Zhenyun Yu",
"Yezhou Yang",
"Yongcheng Jing",
"Junxiao Jiang",
"Mingli Song"
] |
[
"Attribute"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/projection-free-online-optimization-with
|
1802.08183
| null | null |
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity
|
Online optimization has been a successful framework for solving large-scale
problems under computational constraints and partial information. Current
methods for online convex optimization require either a projection or exact
gradient computation at each step, both of which can be prohibitively expensive
for large-scale applications. At the same time, there is a growing trend of
non-convex optimization in machine learning community and a need for online
methods. Continuous DR-submodular functions, which exhibit a natural
diminishing returns condition, have recently been proposed as a broad class of
non-convex functions which may be efficiently optimized. Although online
methods have been introduced, they suffer from similar problems. In this work,
we propose Meta-Frank-Wolfe, the first online projection-free algorithm that
uses stochastic gradient estimates. The algorithm relies on a careful sampling
of gradients in each round and achieves the optimal $O( \sqrt{T})$ adversarial
regret bounds for convex and continuous submodular optimization. We also
propose One-Shot Frank-Wolfe, a simpler algorithm which requires only a single
stochastic gradient estimate in each round and achieves an $O(T^{2/3})$
stochastic regret bound for convex and continuous submodular optimization. We
apply our methods to develop a novel "lifting" framework for the online
discrete submodular maximization and also see that they outperform current
state-of-the-art techniques on various experiments.
| null |
http://arxiv.org/abs/1802.08183v4
|
http://arxiv.org/pdf/1802.08183v4.pdf
|
ICML 2018 7
|
[
"Lin Chen",
"Christopher Harshaw",
"Hamed Hassani",
"Amin Karbasi"
] |
[] | 2018-02-22T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2385
|
http://proceedings.mlr.press/v80/chen18c/chen18c.pdf
|
projection-free-online-optimization-with-1
| null |
[] |
https://paperswithcode.com/paper/multilingual-end-to-end-speech-recognition
|
1806.05059
| null | null |
Multilingual End-to-End Speech Recognition with A Single Transformer on Low-Resource Languages
|
Sequence-to-sequence attention-based models integrate an acoustic,
pronunciation and language model into a single neural network, which make them
very suitable for multilingual automatic speech recognition (ASR). In this
paper, we are concerned with multilingual speech recognition on low-resource
languages by a single Transformer, one of sequence-to-sequence attention-based
models. Sub-words are employed as the multilingual modeling unit without using
any pronunciation lexicon. First, we show that a single multilingual ASR
Transformer performs well on low-resource languages despite of some language
confusion. We then look at incorporating language information into the model by
inserting the language symbol at the beginning or at the end of the original
sub-words sequence under the condition of language information being known
during training. Experiments on CALLHOME datasets demonstrate that the
multilingual ASR Transformer with the language symbol at the end performs
better and can obtain relatively 10.5\% average word error rate (WER) reduction
compared to SHL-MLSTM with residual learning. We go on to show that, assuming
the language information being known during training and testing, about
relatively 12.4\% average WER reduction can be observed compared to SHL-MLSTM
with residual learning through giving the language symbol as the sentence start
token.
| null |
http://arxiv.org/abs/1806.05059v2
|
http://arxiv.org/pdf/1806.05059v2.pdf
| null |
[
"Shiyu Zhou",
"Shuang Xu",
"Bo Xu"
] |
[
"Automatic Speech Recognition",
"Automatic Speech Recognition (ASR)",
"Language Modeling",
"Language Modelling",
"Sentence",
"speech-recognition",
"Speech Recognition"
] | 2018-06-12T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "A **Linear Layer** is a projection $\\mathbf{XW + b}$.",
"full_name": "Linear Layer",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Linear Layer",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Absolute Position Encodings** are a type of position embeddings for [[Transformer](https://paperswithcode.com/method/transformer)-based models] where positional encodings are added to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension $d\\_{model}$ as the embeddings, so that the two can be summed. In the original implementation, sine and cosine functions of different frequencies are used:\r\n\r\n$$ \\text{PE}\\left(pos, 2i\\right) = \\sin\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\n$$ \\text{PE}\\left(pos, 2i+1\\right) = \\cos\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\nwhere $pos$ is the position and $i$ is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from $2\\pi$ to $10000 \\dot 2\\pi$. This function was chosen because the authors hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $\\text{PE}\\_{pos+k}$ can be represented as a linear function of $\\text{PE}\\_{pos}$.\r\n\r\nImage Source: [D2L.ai](https://d2l.ai/chapter_attention-mechanisms/self-attention-and-positional-encoding.html)",
"full_name": "Absolute Position Encodings",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Position Embeddings",
"parent": null
},
"name": "Absolute Position Encodings",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": null,
"description": "**Position-Wise Feed-Forward Layer** is a type of [feedforward layer](https://www.paperswithcode.com/method/category/feedforwad-networks) consisting of two [dense layers](https://www.paperswithcode.com/method/dense-connections) that applies to the last dimension, which means the same dense layers are used for each position item in the sequence, so called position-wise.",
"full_name": "Position-Wise Feed-Forward Layer",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Position-Wise Feed-Forward Layer",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": null,
"description": "**Byte Pair Encoding**, or **BPE**, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).\r\n\r\n[Lei Mao](https://leimao.github.io/blog/Byte-Pair-Encoding/) has a detailed blog post that explains how this works.",
"full_name": "Byte Pair Encoding",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "BPE",
"source_title": "Neural Machine Translation of Rare Words with Subword Units",
"source_url": "http://arxiv.org/abs/1508.07909v5"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Label Smoothing** is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the likelihood of $\\log{p}\\left(y\\mid{x}\\right)$ directly can be harmful. Assume for a small constant $\\epsilon$, the training set label $y$ is correct with probability $1-\\epsilon$ and incorrect otherwise. Label Smoothing regularizes a model based on a [softmax](https://paperswithcode.com/method/softmax) with $k$ output values by replacing the hard $0$ and $1$ classification targets with targets of $\\frac{\\epsilon}{k}$ and $1-\\frac{k-1}{k}\\epsilon$ respectively.\r\n\r\nSource: Deep Learning, Goodfellow et al\r\n\r\nImage Source: [When Does Label Smoothing Help?](https://arxiv.org/abs/1906.02629)",
"full_name": "Label Smoothing",
"introduced_year": 1985,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Label Smoothing",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.com/method/rmsprop) and [SGD w/th Momentum](https://paperswithcode.com/method/sgd-with-momentum). The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. \r\n\r\nThe weight updates are performed as:\r\n\r\n$$ w_{t} = w_{t-1} - \\eta\\frac{\\hat{m}\\_{t}}{\\sqrt{\\hat{v}\\_{t}} + \\epsilon} $$\r\n\r\nwith\r\n\r\n$$ \\hat{m}\\_{t} = \\frac{m_{t}}{1-\\beta^{t}_{1}} $$\r\n\r\n$$ \\hat{v}\\_{t} = \\frac{v_{t}}{1-\\beta^{t}_{2}} $$\r\n\r\n$$ m_{t} = \\beta_{1}m_{t-1} + (1-\\beta_{1})g_{t} $$\r\n\r\n$$ v_{t} = \\beta_{2}v_{t-1} + (1-\\beta_{2})g_{t}^{2} $$\r\n\r\n\r\n$ \\eta $ is the step size/learning rate, around 1e-3 in the original paper. $ \\epsilon $ is a small number, typically 1e-8 or 1e-10, to prevent dividing by zero. $ \\beta_{1} $ and $ \\beta_{2} $ are forgetting parameters, with typical values 0.9 and 0.999, respectively.",
"full_name": "Adam",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "Adam",
"source_title": "Adam: A Method for Stochastic Optimization",
"source_url": "http://arxiv.org/abs/1412.6980v9"
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/fec78a687210851f055f792d45300d27cc60ae41/transformer/SubLayers.py#L9",
"description": "**Multi-head Attention** is a module for attention mechanisms which runs through an attention mechanism several times in parallel. The independent attention outputs are then concatenated and linearly transformed into the expected dimension. Intuitively, multiple attention heads allows for attending to parts of the sequence differently (e.g. longer-term dependencies versus shorter-term dependencies). \r\n\r\n$$ \\text{MultiHead}\\left(\\textbf{Q}, \\textbf{K}, \\textbf{V}\\right) = \\left[\\text{head}\\_{1},\\dots,\\text{head}\\_{h}\\right]\\textbf{W}_{0}$$\r\n\r\n$$\\text{where} \\text{ head}\\_{i} = \\text{Attention} \\left(\\textbf{Q}\\textbf{W}\\_{i}^{Q}, \\textbf{K}\\textbf{W}\\_{i}^{K}, \\textbf{V}\\textbf{W}\\_{i}^{V} \\right) $$\r\n\r\nAbove $\\textbf{W}$ are all learnable parameter matrices.\r\n\r\nNote that [scaled dot-product attention](https://paperswithcode.com/method/scaled) is most commonly used in this module, although in principle it can be swapped out for other types of attention mechanism.\r\n\r\nSource: [Lilian Weng](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html#a-family-of-attention-mechanisms)",
"full_name": "Multi-Head Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Modules** refer to modules that incorporate attention mechanisms. For example, multi-head attention is a module that incorporates multiple attention heads. Below you can find a continuously updating list of attention modules.",
"name": "Attention Modules",
"parent": "Attention"
},
"name": "Multi-Head Attention",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": "",
"description": "",
"full_name": "Attention Is All You Need",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "If you're looking to get in touch with American Airlines fast, ☎️+1-801-(855)-(5905)or +1-804-853-9001✅ there are\r\nseveral efficient ways to reach their customer service team. The quickest method is to dial ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. American’s phone service ensures that you can speak with a live\r\nrepresentative promptly to resolve any issues or queries regarding your booking, reservation,\r\nor any changes, such as name corrections or ticket cancellations.",
"name": "Attention Mechanisms",
"parent": "Attention"
},
"name": "Attention",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/tunz/transformer-pytorch/blob/e7266679f0b32fd99135ea617213f986ceede056/model/transformer.py#L201",
"description": "A **Transformer** is a model architecture that eschews recurrence and instead relies entirely on an [attention mechanism](https://paperswithcode.com/methods/category/attention-mechanisms-1) to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favor of [attention mechanisms](https://paperswithcode.com/methods/category/attention-mechanisms-1) allows for significantly more parallelization than methods like [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and [CNNs](https://paperswithcode.com/methods/category/convolutional-neural-networks).",
"full_name": "Transformer",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Transformer",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
}
] |
https://paperswithcode.com/paper/deep-reinforcement-learning-for-dynamic-urban
|
1806.05310
| null | null |
Deep Reinforcement Learning for Dynamic Urban Transportation Problems
|
We explore the use of deep learning and deep reinforcement learning for
optimization problems in transportation. Many transportation system analysis
tasks are formulated as an optimization problem - such as optimal control
problems in intelligent transportation systems and long term urban planning.
Often transportation models used to represent dynamics of a transportation
system involve large data sets with complex input-output interactions and are
difficult to use in the context of optimization. Use of deep learning
metamodels can produce a lower dimensional representation of those relations
and allow to implement optimization and reinforcement learning algorithms in an
efficient manner. In particular, we develop deep learning models for
calibrating transportation simulators and for reinforcement learning to solve
the problem of optimal scheduling of travelers on the network.
| null |
http://arxiv.org/abs/1806.05310v1
|
http://arxiv.org/pdf/1806.05310v1.pdf
| null |
[
"Laura Schultz",
"Vadim Sokolov"
] |
[
"Deep Learning",
"Deep Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)",
"Scheduling"
] | 2018-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/stress-test-evaluation-for-natural-language
|
1806.00692
| null | null |
Stress Test Evaluation for Natural Language Inference
|
Natural language inference (NLI) is the task of determining if a natural
language hypothesis can be inferred from a given premise in a justifiable
manner. NLI was proposed as a benchmark task for natural language
understanding. Existing models perform well at standard datasets for NLI,
achieving impressive results across different genres of text. However, the
extent to which these models understand the semantic content of sentences is
unclear. In this work, we propose an evaluation methodology consisting of
automatically constructed "stress tests" that allow us to examine whether
systems have the ability to make real inferential decisions. Our evaluation of
six sentence-encoder models on these stress tests reveals strengths and
weaknesses of these models with respect to challenging linguistic phenomena,
and suggests important directions for future work in this area.
|
Natural language inference (NLI) is the task of determining if a natural language hypothesis can be inferred from a given premise in a justifiable manner.
|
http://arxiv.org/abs/1806.00692v3
|
http://arxiv.org/pdf/1806.00692v3.pdf
|
COLING 2018 8
|
[
"Aakanksha Naik",
"Abhilasha Ravichander",
"Norman Sadeh",
"Carolyn Rose",
"Graham Neubig"
] |
[
"Natural Language Inference",
"Natural Language Understanding",
"Sentence"
] | 2018-06-02T00:00:00 |
https://aclanthology.org/C18-1198
|
https://aclanthology.org/C18-1198.pdf
|
stress-test-evaluation-for-natural-language-2
| null |
[] |
https://paperswithcode.com/paper/geometric-shape-features-extraction-using-a
|
1806.05299
| null | null |
Geometric Shape Features Extraction Using a Steady State Partial Differential Equation System
|
A unified method for extracting geometric shape features from binary image
data using a steady state partial differential equation (PDE) system as a
boundary value problem is presented in this paper. The PDE and functions are
formulated to extract the thickness, orientation, and skeleton simultaneously.
The main advantages of the proposed method is that the orientation is defined
without derivatives and thickness computation is not imposed a topological
constraint on the target shape. A one-dimensional analytical solution is
provided to validate the proposed method. In addition, two-dimensional
numerical examples are presented to confirm the usefulness of the proposed
method.
| null |
http://arxiv.org/abs/1806.05299v3
|
http://arxiv.org/pdf/1806.05299v3.pdf
| null |
[
"Takayuki Yamada"
] |
[] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/apuntes-de-redes-neuronales-artificiales
|
1806.05298
| null | null |
Apuntes de Redes Neuronales Artificiales
|
These handouts are designed for people who is just starting involved with the
topic artificial neural networks. We show how it works a single artificial
neuron (McCulloch & Pitt model), mathematically and graphically. We do explain
the delta rule, a learning algorithm to find the neuron weights. We also
present some examples in MATLAB/Octave. There are examples for classification
task for lineal and non-lineal problems. At the end, we present an artificial
neural network, a feed-forward neural network along its learning algorithm
backpropagation.
-----
Estos apuntes est\'an dise\~nados para personas que por primera vez se
introducen en el tema de las redes neuronales artificiales. Se muestra el
funcionamiento b\'asico de una neurona, matem\'aticamente y gr\'aficamente. Se
explica la Regla Delta, algoritmo deaprendizaje para encontrar los pesos de una
neurona. Tambi\'en se muestran ejemplos en MATLAB/Octave. Hay ejemplos para
problemas de clasificaci\'on, para problemas lineales y no-lineales. En la
parte final se muestra la arquitectura de red neuronal artificial conocida como
backpropagation.
| null |
http://arxiv.org/abs/1806.05298v1
|
http://arxiv.org/pdf/1806.05298v1.pdf
| null |
[
"J. C. Cuevas-Tello"
] |
[] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/pattern-dependence-detection-using-n-tarp
|
1806.05297
| null | null |
Pattern Dependence Detection using n-TARP Clustering
|
Consider an experiment involving a potentially small number of subjects. Some
random variables are observed on each subject: a high-dimensional one called
the "observed" random variable, and a one-dimensional one called the "outcome"
random variable. We are interested in the dependencies between the observed
random variable and the outcome random variable. We propose a method to
quantify and validate the dependencies of the outcome random variable on the
various patterns contained in the observed random variable. Different degrees
of relationship are explored (linear, quadratic, cubic, ...). This work is
motivated by the need to analyze educational data, which often involves
high-dimensional data representing a small number of students. Thus our
implementation is designed for a small number of subjects; however, it can be
easily modified to handle a very large dataset. As an illustration, the
proposed method is used to study the influence of certain skills on the course
grade of students in a signal processing class. A valid dependency of the grade
on the different skill patterns is observed in the data.
| null |
http://arxiv.org/abs/1806.05297v1
|
http://arxiv.org/pdf/1806.05297v1.pdf
| null |
[
"Tarun Yellamraju",
"Mireille Boutin"
] |
[
"Clustering",
"valid"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/projecting-embeddings-for-domain-adaptation
|
1806.04381
| null | null |
Projecting Embeddings for Domain Adaptation: Joint Modeling of Sentiment Analysis in Diverse Domains
|
Domain adaptation for sentiment analysis is challenging due to the fact that
supervised classifiers are very sensitive to changes in domain. The two most
prominent approaches to this problem are structural correspondence learning and
autoencoders. However, they either require long training times or suffer
greatly on highly divergent domains. Inspired by recent advances in
cross-lingual sentiment analysis, we provide a novel perspective and cast the
domain adaptation problem as an embedding projection task. Our model takes as
input two mono-domain embedding spaces and learns to project them to a
bi-domain space, which is jointly optimized to (1) project across domains and
to (2) predict sentiment. We perform domain adaptation experiments on 20
source-target domain pairs for sentiment classification and report novel
state-of-the-art results on 11 domain pairs, including the Amazon domain
adaptation datasets and SemEval 2013 and 2016 datasets. Our analysis shows that
our model performs comparably to state-of-the-art approaches on domains that
are similar, while performing significantly better on highly divergent domains.
Our code is available at https://github.com/jbarnesspain/domain_blse
|
Our analysis shows that our model performs comparably to state-of-the-art approaches on domains that are similar, while performing significantly better on highly divergent domains.
|
http://arxiv.org/abs/1806.04381v2
|
http://arxiv.org/pdf/1806.04381v2.pdf
| null |
[
"Jeremy Barnes",
"Roman Klinger",
"Sabine Schulte im Walde"
] |
[
"Domain Adaptation",
"Sentiment Analysis",
"Sentiment Classification"
] | 2018-06-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/patternnet-visual-pattern-mining-with-deep
|
1703.06339
| null | null |
PatternNet: Visual Pattern Mining with Deep Neural Network
|
Visual patterns represent the discernible regularity in the visual world.
They capture the essential nature of visual objects or scenes. Understanding
and modeling visual patterns is a fundamental problem in visual recognition
that has wide ranging applications. In this paper, we study the problem of
visual pattern mining and propose a novel deep neural network architecture
called PatternNet for discovering these patterns that are both discriminative
and representative. The proposed PatternNet leverages the filters in the last
convolution layer of a convolutional neural network to find locally consistent
visual patches, and by combining these filters we can effectively discover
unique visual patterns. In addition, PatternNet can discover visual patterns
efficiently without performing expensive image patch sampling, and this
advantage provides an order of magnitude speedup compared to most other
approaches. We evaluate the proposed PatternNet subjectively by showing
randomly selected visual patterns which are discovered by our method and
quantitatively by performing image classification with the identified visual
patterns and comparing our performance with the current state-of-the-art. We
also directly evaluate the quality of the discovered visual patterns by
leveraging the identified patterns as proposed objects in an image and compare
with other relevant methods. Our proposed network and procedure, PatterNet, is
able to outperform competing methods for the tasks described.
| null |
http://arxiv.org/abs/1703.06339v2
|
http://arxiv.org/pdf/1703.06339v2.pdf
| null |
[
"Hongzhi Li",
"Joseph G. Ellis",
"Lei Zhang",
"Shih-Fu Chang"
] |
[
"image-classification",
"Image Classification"
] | 2017-03-18T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/automatic-formation-of-the-structure-of
|
1806.05292
| null | null |
Automatic formation of the structure of abstract machines in hierarchical reinforcement learning with state clustering
|
We introduce a new approach to hierarchy formation and task decomposition in
hierarchical reinforcement learning. Our method is based on the Hierarchy Of
Abstract Machines (HAM) framework because HAM approach is able to design
efficient controllers that will realize specific behaviors in real robots. The
key to our algorithm is the introduction of the internal or "mental"
environment in which the state represents the structure of the HAM hierarchy.
The internal action in this environment leads to changes the hierarchy of HAMs.
We propose the classical Q-learning procedure in the internal environment which
allows the agent to obtain an optimal hierarchy. We extends the HAM framework
by adding on-model approach to select the appropriate sub-machine to execute
action sequences for certain class of external environment states. Preliminary
experiments demonstrated the prospects of the method.
| null |
http://arxiv.org/abs/1806.05292v1
|
http://arxiv.org/pdf/1806.05292v1.pdf
| null |
[
"Aleksandr I. Panov",
"Aleksey Skrynnik"
] |
[
"Clustering",
"Hierarchical Reinforcement Learning",
"Q-Learning",
"Reinforcement Learning"
] | 2018-06-13T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Q-Learning** is an off-policy temporal difference control algorithm:\r\n\r\n$$Q\\left(S\\_{t}, A\\_{t}\\right) \\leftarrow Q\\left(S\\_{t}, A\\_{t}\\right) + \\alpha\\left[R_{t+1} + \\gamma\\max\\_{a}Q\\left(S\\_{t+1}, a\\right) - Q\\left(S\\_{t}, A\\_{t}\\right)\\right] $$\r\n\r\nThe learned action-value function $Q$ directly approximates $q\\_{*}$, the optimal action-value function, independent of the policy being followed.\r\n\r\nSource: Sutton and Barto, Reinforcement Learning, 2nd Edition",
"full_name": "Q-Learning",
"introduced_year": 1984,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Off-Policy TD Control",
"parent": null
},
"name": "Q-Learning",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/natural-language-to-structured-query
|
1803.02400
| null | null |
Natural Language to Structured Query Generation via Meta-Learning
|
In conventional supervised training, a model is trained to fit all the
training examples. However, having a monolithic model may not always be the
best strategy, as examples could vary widely. In this work, we explore a
different learning protocol that treats each example as a unique pseudo-task,
by reducing the original learning problem to a few-shot meta-learning scenario
with the help of a domain-dependent relevance function. When evaluated on the
WikiSQL dataset, our approach leads to faster convergence and achieves
1.1%-5.4% absolute accuracy gains over the non-meta-learning counterparts.
|
In conventional supervised training, a model is trained to fit all the training examples.
|
http://arxiv.org/abs/1803.02400v4
|
http://arxiv.org/pdf/1803.02400v4.pdf
|
NAACL 2018 6
|
[
"Po-Sen Huang",
"Chenglong Wang",
"Rishabh Singh",
"Wen-tau Yih",
"Xiaodong He"
] |
[
"Meta-Learning"
] | 2018-03-02T00:00:00 |
https://aclanthology.org/N18-2115
|
https://aclanthology.org/N18-2115.pdf
|
natural-language-to-structured-query-1
| null |
[] |
https://paperswithcode.com/paper/a-flexible-convolutional-solver-with
|
1806.05285
| null | null |
A Flexible Convolutional Solver with Application to Photorealistic Style Transfer
|
We propose a new flexible deep convolutional neural network (convnet) to
perform fast visual style transfer. In contrast to existing convnets that
address the same task, our architecture derives directly from the structure of
the gradient descent originally used to solve the style transfer problem [Gatys
et al., 2016]. Like existing convnets, ours approximately solves the original
problem much faster than the gradient descent. However, our network is uniquely
flexible by design: it can be manipulated at runtime to enforce new constraints
on the final solution. In particular, we show how to modify it to obtain a
photorealistic result with no retraining. We study the modifications made by
[Luan et al., 2017] to the original cost function of [Gatys et al., 2016] to
achieve photorealistic style transfer. These modifications affect directly the
gradient descent and can be reported on-the-fly in our network. These
modifications are possible as the proposed architecture stems from unrolling
the gradient descent.
| null |
http://arxiv.org/abs/1806.05285v1
|
http://arxiv.org/pdf/1806.05285v1.pdf
| null |
[
"Gilles Puy",
"Patrick Pérez"
] |
[
"Rolling Shutter Correction",
"Style Transfer"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/how-predictable-is-your-state-leveraging
|
1806.05284
| null | null |
How Predictable is Your State? Leveraging Lexical and Contextual Information for Predicting Legislative Floor Action at the State Level
|
Modeling U.S. Congressional legislation and roll-call votes has received
significant attention in previous literature. However, while legislators across
50 state governments and D.C. propose over 100,000 bills each year, and on
average enact over 30% of them, state level analysis has received relatively
less attention due in part to the difficulty in obtaining the necessary data.
Since each state legislature is guided by their own procedures, politics and
issues, however, it is difficult to qualitatively asses the factors that affect
the likelihood of a legislative initiative succeeding. Herein, we present
several methods for modeling the likelihood of a bill receiving floor action
across all 50 states and D.C. We utilize the lexical content of over 1 million
bills, along with contextual legislature and legislator derived features to
build our predictive models, allowing a comparison of the factors that are
important to the lawmaking process. Furthermore, we show that these signals
hold complementary predictive power, together achieving an average improvement
in accuracy of 18% over state specific baselines.
| null |
http://arxiv.org/abs/1806.05284v1
|
http://arxiv.org/pdf/1806.05284v1.pdf
|
COLING 2018 8
|
[
"Vlad Eidelman",
"Anastassia Kornilova",
"Daniel Argyle"
] |
[] | 2018-06-13T00:00:00 |
https://aclanthology.org/C18-1013
|
https://aclanthology.org/C18-1013.pdf
|
how-predictable-is-your-state-leveraging-1
| null |
[] |
https://paperswithcode.com/paper/solving-the-steiner-tree-problem-in-graphs
|
1806.06685
| null | null |
Solving the Steiner Tree Problem in graphs with Variable Neighborhood Descent
|
The Steiner Tree Problem (STP) in graphs is an important problem with various
applications in many areas such as design of integrated circuits, evolution
theory, networking, etc. In this paper, we propose an algorithm to solve the
STP. The algorithm includes a reducer and a solver using Variable Neighborhood
Descent (VND), interacting with each other during the search. New constructive
heuristics and a vertex score system for intensification purpose are proposed.
The algorithm is tested on a set of benchmarks which shows encouraging results.
| null |
http://arxiv.org/abs/1806.06685v1
|
http://arxiv.org/pdf/1806.06685v1.pdf
| null |
[
"Matthieu De Laere",
"San Tu Pham",
"Patrick De Causmaecker"
] |
[
"Steiner Tree Problem"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-privacy-preserving-encodings-through
|
1802.05214
| null | null |
Learning Privacy Preserving Encodings through Adversarial Training
|
We present a framework to learn privacy-preserving encodings of images that
inhibit inference of chosen private attributes, while allowing recovery of
other desirable information. Rather than simply inhibiting a given fixed
pre-trained estimator, our goal is that an estimator be unable to learn to
accurately predict the private attributes even with knowledge of the encoding
function. We use a natural adversarial optimization-based formulation for
this---training the encoding function against a classifier for the private
attribute, with both modeled as deep neural networks. The key contribution of
our work is a stable and convergent optimization approach that is successful at
learning an encoder with our desired properties---maintaining utility while
inhibiting inference of private attributes, not just within the adversarial
optimization, but also by classifiers that are trained after the encoder is
fixed. We adopt a rigorous experimental protocol for verification wherein
classifiers are trained exhaustively till saturation on the fixed encoders. We
evaluate our approach on tasks of real-world complexity---learning
high-dimensional encodings that inhibit detection of different scene
categories---and find that it yields encoders that are resilient at maintaining
privacy.
| null |
http://arxiv.org/abs/1802.05214v3
|
http://arxiv.org/pdf/1802.05214v3.pdf
| null |
[
"Francesco Pittaluga",
"Sanjeev J. Koppal",
"Ayan Chakrabarti"
] |
[
"Attribute",
"Privacy Preserving"
] | 2018-02-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/online-learning-over-a-finite-action-set-with
|
1803.01548
| null | null |
Online learning over a finite action set with limited switching
|
This paper studies the value of switching actions in the Prediction From
Experts (PFE) problem and Adversarial Multi-Armed Bandits (MAB) problem. First,
we revisit the well-studied and practically motivated setting of PFE with
switching costs. Many algorithms are known to achieve the minimax optimal order
of $O(\sqrt{T \log n})$ in expectation for both regret and number of switches,
where $T$ is the number of iterations and $n$ the number of actions. However,
no high probability (h.p.) guarantees are known. Our main technical
contribution is the first algorithms which with h.p. achieve this optimal order
for both regret and switches. This settles an open problem of [Devroye et al.,
2015], and directly implies the first h.p. guarantees for several problems of
interest.
Next, to investigate the value of switching actions at a more granular level,
we introduce the setting of switching budgets, in which algorithms are limited
to $S \leq T$ switches between actions. This entails a limited number of free
switches, in contrast to the unlimited number of expensive switches in the
switching cost setting. Using the above result and several reductions, we unify
previous work and completely characterize the complexity of this switching
budget setting up to small polylogarithmic factors: for both PFE and MAB, for
all switching budgets $S \leq T$, and for both expectation and h.p. guarantees.
For PFE, we show the optimal rate is $\tilde{\Theta}(\sqrt{T\log n})$ for $S =
\Omega(\sqrt{T\log n})$, and $\min(\tilde{\Theta}(\tfrac{T\log n}{S}), T)$ for
$S = O(\sqrt{T \log n})$. Interestingly, the bandit setting does not exhibit
such a phase transition; instead we show the minimax rate decays steadily as
$\min(\tilde{\Theta}(\tfrac{T\sqrt{n}}{\sqrt{S}}), T)$ for all ranges of $S
\leq T$. These results recover and generalize the known minimax rates for the
(arbitrary) switching cost setting.
| null |
http://arxiv.org/abs/1803.01548v2
|
http://arxiv.org/pdf/1803.01548v2.pdf
| null |
[
"Jason Altschuler",
"Kunal Talwar"
] |
[
"Multi-Armed Bandits"
] | 2018-03-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/benchmarks-for-image-classification-and-other
|
1806.05272
| null | null |
Benchmarks for Image Classification and Other High-dimensional Pattern Recognition Problems
|
A good classification method should yield more accurate results than simple
heuristics. But there are classification problems, especially high-dimensional
ones like the ones based on image/video data, for which simple heuristics can
work quite accurately; the structure of the data in such problems is easy to
uncover without any sophisticated or computationally expensive method. On the
other hand, some problems have a structure that can only be found with
sophisticated pattern recognition methods. We are interested in quantifying the
difficulty of a given high-dimensional pattern recognition problem. We consider
the case where the patterns come from two pre-determined classes and where the
objects are represented by points in a high-dimensional vector space. However,
the framework we propose is extendable to an arbitrarily large number of
classes. We propose classification benchmarks based on simple random projection
heuristics. Our benchmarks are 2D curves parameterized by the classification
error and computational cost of these simple heuristics. Each curve divides the
plane into a "positive- gain" and a "negative-gain" region. The latter contains
methods that are ill-suited for the given classification problem. The former is
divided into two by the curve asymptote; methods that lie in the small region
under the curve but right of the asymptote merely provide a computational gain
but no structural advantage over the random heuristics. We prove that the curve
asymptotes are optimal (i.e. at Bayes error) in some cases, and thus no
sophisticated method can provide a structural advantage over the random
heuristics. Such classification problems, an example of which we present in our
numerical experiments, provide poor ground for testing new pattern
classification methods.
| null |
http://arxiv.org/abs/1806.05272v1
|
http://arxiv.org/pdf/1806.05272v1.pdf
| null |
[
"Tarun Yellamraju",
"Jonas Hepp",
"Mireille Boutin"
] |
[
"Classification",
"General Classification",
"image-classification",
"Image Classification",
"Vocal Bursts Intensity Prediction"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/structured-variational-learning-of-bayesian
|
1806.05975
| null | null |
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors
|
Bayesian Neural Networks (BNNs) have recently received increasing attention
for their ability to provide well-calibrated posterior uncertainties. However,
model selection---even choosing the number of nodes---remains an open question.
Recent work has proposed the use of a horseshoe prior over node pre-activations
of a Bayesian neural network, which effectively turns off nodes that do not
help explain the data. In this work, we propose several modeling and inference
advances that consistently improve the compactness of the model learned while
maintaining predictive performance, especially in smaller-sample settings
including reinforcement learning.
|
Bayesian Neural Networks (BNNs) have recently received increasing attention for their ability to provide well-calibrated posterior uncertainties.
|
http://arxiv.org/abs/1806.05975v2
|
http://arxiv.org/pdf/1806.05975v2.pdf
|
ICML 2018 7
|
[
"Soumya Ghosh",
"Jiayu Yao",
"Finale Doshi-Velez"
] |
[
"Model Selection",
"Open-Ended Question Answering",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-13T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2321
|
http://proceedings.mlr.press/v80/ghosh18a/ghosh18a.pdf
|
structured-variational-learning-of-bayesian-1
| null |
[] |
https://paperswithcode.com/paper/online-self-supervised-scene-segmentation-for
|
1806.05269
| null | null |
Online Self-supervised Scene Segmentation for Micro Aerial Vehicles
|
Recently, there have been numerous advances in the development of payload and
power constrained lightweight Micro Aerial Vehicles (MAVs). As these robots
aspire for high-speed autonomous flights in complex dynamic environments,
robust scene understanding at long-range becomes critical. The problem is
heavily characterized by either the limitations imposed by sensor capabilities
for geometry-based methods, or the need for large-amounts of manually annotated
training data required by data-driven methods. This motivates the need to build
systems that have the capability to alleviate these problems by exploiting the
complimentary strengths of both geometry and data-driven methods. In this
paper, we take a step in this direction and propose a generic framework for
adaptive scene segmentation using self-supervised online learning. We present
this in the context of vision-based autonomous MAV flight, and demonstrate the
efficacy of our proposed system through extensive experiments on benchmark
datasets and real-world field tests.
| null |
http://arxiv.org/abs/1806.05269v1
|
http://arxiv.org/pdf/1806.05269v1.pdf
| null |
[
"Shreyansh Daftry",
"Yashasvi Agrawal",
"Larry Matthies"
] |
[
"Scene Segmentation",
"Scene Understanding"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-framework-for-validating-models-of-evasion
|
1708.08327
| null | null |
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features
|
Machine learning (ML) techniques are increasingly common in security applications, such as malware and intrusion detection. However, ML models are often susceptible to evasion attacks, in which an adversary makes changes to the input (such as malware) in order to avoid being detected. A conventional approach to evaluate ML robustness to such attacks, as well as to design robust ML, is by considering simplified feature-space models of attacks, where the attacker changes ML features directly to effect evasion, while minimizing or constraining the magnitude of this change. We investigate the effectiveness of this approach to designing robust ML in the face of attacks that can be realized in actual malware (realizable attacks). We demonstrate that in the context of structure-based PDF malware detection, such techniques appear to have limited effectiveness, but they are effective with content-based detectors. In either case, we show that augmenting the feature space models with conserved features (those that cannot be unilaterally modified without compromising malicious functionality) significantly improves performance. Finally, we show that feature space models enable generalized robustness when faced with a variety of realizable attacks, as compared to classifiers which are tuned to be robust to a specific realizable attack.
|
A conventional approach to evaluate ML robustness to such attacks, as well as to design robust ML, is by considering simplified feature-space models of attacks, where the attacker changes ML features directly to effect evasion, while minimizing or constraining the magnitude of this change.
|
https://arxiv.org/abs/1708.08327v5
|
https://arxiv.org/pdf/1708.08327v5.pdf
| null |
[
"Liang Tong",
"Bo Li",
"Chen Hajaj",
"Chaowei Xiao",
"Ning Zhang",
"Yevgeniy Vorobeychik"
] |
[
"Intrusion Detection",
"Malware Detection"
] | 2017-08-28T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/on-the-connection-between-learning-two-layers
|
1802.07301
| null | null |
On the Connection Between Learning Two-Layers Neural Networks and Tensor Decomposition
|
We establish connections between the problem of learning a two-layer neural
network and tensor decomposition. We consider a model with feature vectors
$\boldsymbol x \in \mathbb R^d$, $r$ hidden units with weights $\{\boldsymbol
w_i\}_{1\le i \le r}$ and output $y\in \mathbb R$, i.e., $y=\sum_{i=1}^r
\sigma( \boldsymbol w_i^{\mathsf T}\boldsymbol x)$, with activation functions
given by low-degree polynomials. In particular, if $\sigma(x) =
a_0+a_1x+a_3x^3$, we prove that no polynomial-time learning algorithm can
outperform the trivial predictor that assigns to each example the response
variable $\mathbb E(y)$, when $d^{3/2}\ll r\ll d^2$. Our conclusion holds for a
`natural data distribution', namely standard Gaussian feature vectors
$\boldsymbol x$, and output distributed according to a two-layer neural network
with random isotropic weights, and under a certain complexity-theoretic
assumption on tensor decomposition. Roughly speaking, we assume that no
polynomial-time algorithm can substantially outperform current methods for
tensor decomposition based on the sum-of-squares hierarchy.
We also prove generalizations of this statement for higher degree polynomial
activations, and non-random weight vectors. Remarkably, several existing
algorithms for learning two-layer networks with rigorous guarantees are based
on tensor decomposition. Our results support the idea that this is indeed the
core computational difficulty in learning such networks, under the stated
generative model for the data. As a side result, we show that under this model
learning the network requires accurate learning of its weights, a property that
does not hold in a more general setting.
| null |
http://arxiv.org/abs/1802.07301v3
|
http://arxiv.org/pdf/1802.07301v3.pdf
| null |
[
"Marco Mondelli",
"Andrea Montanari"
] |
[
"Tensor Decomposition"
] | 2018-02-20T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-comparison-of-methods-for-model-selection
|
1804.05146
| null | null |
A comparison of methods for model selection when estimating individual treatment effects
|
Practitioners in medicine, business, political science, and other fields are
increasingly aware that decisions should be personalized to each patient,
customer, or voter. A given treatment (e.g. a drug or advertisement) should be
administered only to those who will respond most positively, and certainly not
to those who will be harmed by it. Individual-level treatment effects can be
estimated with tools adapted from machine learning, but different models can
yield contradictory estimates. Unlike risk prediction models, however,
treatment effect models cannot be easily evaluated against each other using a
held-out test set because the true treatment effect itself is never directly
observed. Besides outcome prediction accuracy, several metrics that can
leverage held-out data to evaluate treatment effects models have been proposed,
but they are not widely used. We provide a didactic framework that elucidates
the relationships between the different approaches and compare them all using a
variety of simulations of both randomized and observational data. Our results
show that researchers estimating heterogenous treatment effects need not limit
themselves to a single model-fitting algorithm. Instead of relying on a single
method, multiple models fit by a diverse set of algorithms should be evaluated
against each other using an objective function learned from the validation set.
The model minimizing that objective should be used for estimating the
individual treatment effect for future individuals.
|
Instead of relying on a single method, multiple models fit by a diverse set of algorithms should be evaluated against each other using an objective function learned from the validation set.
|
http://arxiv.org/abs/1804.05146v2
|
http://arxiv.org/pdf/1804.05146v2.pdf
| null |
[
"Alejandro Schuler",
"Michael Baiocchi",
"Robert Tibshirani",
"Nigam Shah"
] |
[
"Model Selection"
] | 2018-04-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/embarrassingly-parallel-inference-for
|
1702.08420
| null | null |
Embarrassingly Parallel Inference for Gaussian Processes
|
Training Gaussian process-based models typically involves an $ O(N^3)$ computational bottleneck due to inverting the covariance matrix. Popular methods for overcoming this matrix inversion problem cannot adequately model all types of latent functions, and are often not parallelizable. However, judicious choice of model structure can ameliorate this problem. A mixture-of-experts model that uses a mixture of $K$ Gaussian processes offers modeling flexibility and opportunities for scalable inference. Our embarrassingly parallel algorithm combines low-dimensional matrix inversions with importance sampling to yield a flexible, scalable mixture-of-experts model that offers comparable performance to Gaussian process regression at a much lower computational cost.
|
Training Gaussian process-based models typically involves an $ O(N^3)$ computational bottleneck due to inverting the covariance matrix.
|
https://arxiv.org/abs/1702.08420v9
|
https://arxiv.org/pdf/1702.08420v9.pdf
| null |
[
"Michael Minyi Zhang",
"Sinead A. Williamson"
] |
[
"Gaussian Processes",
"Mixture-of-Experts",
"regression"
] | 2017-02-27T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Gaussian Processes** are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for an unseen point from training data. The models are fully probabilistic so uncertainty bounds are baked in with the model.\r\n\r\nImage Source: Gaussian Processes for Machine Learning, C. E. Rasmussen & C. K. I. Williams",
"full_name": "Gaussian Process",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Non-Parametric Classification** methods perform classification where we use non-parametric methods to approximate the functional form of the relationship. Below you can find a continuously updating list of non-parametric classification methods.",
"name": "Non-Parametric Classification",
"parent": null
},
"name": "Gaussian Process",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/smhd-a-large-scale-resource-for-exploring
|
1806.05258
| null | null |
SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions
|
Mental health is a significant and growing public health concern. As language
usage can be leveraged to obtain crucial insights into mental health
conditions, there is a need for large-scale, labeled, mental health-related
datasets of users who have been diagnosed with one or more of such conditions.
In this paper, we investigate the creation of high-precision patterns to
identify self-reported diagnoses of nine different mental health conditions,
and obtain high-quality labeled data without the need for manual labelling. We
introduce the SMHD (Self-reported Mental Health Diagnoses) dataset and make it
available. SMHD is a novel large dataset of social media posts from users with
one or multiple mental health conditions along with matched control users. We
examine distinctions in users' language, as measured by linguistic and
psychological variables. We further explore text classification methods to
identify individuals with mental conditions through their language.
|
Mental health is a significant and growing public health concern.
|
http://arxiv.org/abs/1806.05258v2
|
http://arxiv.org/pdf/1806.05258v2.pdf
|
COLING 2018 8
|
[
"Arman Cohan",
"Bart Desmet",
"Andrew Yates",
"Luca Soldaini",
"Sean MacAvaney",
"Nazli Goharian"
] |
[
"text-classification",
"Text Classification"
] | 2018-06-13T00:00:00 |
https://aclanthology.org/C18-1126
|
https://aclanthology.org/C18-1126.pdf
|
smhd-a-large-scale-resource-for-exploring-1
| null |
[] |
https://paperswithcode.com/paper/finding-your-lookalike-measuring-face
|
1806.05252
| null | null |
Finding your Lookalike: Measuring Face Similarity Rather than Face Identity
|
Face images are one of the main areas of focus for computer vision, receiving
on a wide variety of tasks. Although face recognition is probably the most
widely researched, many other tasks such as kinship detection, facial
expression classification and facial aging have been examined. In this work we
propose the new, subjective task of quantifying perceived face similarity
between a pair of faces. That is, we predict the perceived similarity between
facial images, given that they are not of the same person. Although this task
is clearly correlated with face recognition, it is different and therefore
justifies a separate investigation. Humans often remark that two persons look
alike, even in cases where the persons are not actually confused with one
another. In addition, because face similarity is different than traditional
image similarity, there are challenges in data collection and labeling, and
dealing with diverging subjective opinions between human labelers. We present
evidence that finding facial look-alikes and recognizing faces are two distinct
tasks. We propose a new dataset for facial similarity and introduce the
Lookalike network, directed towards similar face classification, which
outperforms the ad hoc usage of a face recognition network directed at the same
task.
| null |
http://arxiv.org/abs/1806.05252v1
|
http://arxiv.org/pdf/1806.05252v1.pdf
| null |
[
"Amir Sadovnik",
"Wassim Gharbi",
"Thanh Vu",
"Andrew Gallagher"
] |
[
"Face Recognition",
"General Classification"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/what-about-applied-fairness
|
1806.05250
| null | null |
What About Applied Fairness?
|
Machine learning practitioners are often ambivalent about the ethical aspects
of their products. We believe anything that gets us from that current state to
one in which our systems are achieving some degree of fairness is an
improvement that should be welcomed. This is true even when that progress does
not get us 100% of the way to the goal of "complete" fairness or perfectly
align with our personal belief on which measure of fairness is used. Some
measure of fairness being built would still put us in a better position than
the status quo. Impediments to getting fairness and ethical concerns applied in
real applications, whether they are abstruse philosophical debates or technical
overhead such as the introduction of ever more hyper-parameters, should be
avoided. In this paper we further elaborate on our argument for this viewpoint
and its importance.
| null |
http://arxiv.org/abs/1806.05250v1
|
http://arxiv.org/pdf/1806.05250v1.pdf
| null |
[
"Jared Sylvester",
"Edward Raff"
] |
[
"Fairness",
"Position"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/boosted-training-of-convolutional-neural
|
1806.05974
| null | null |
Boosted Training of Convolutional Neural Networks for Multi-Class Segmentation
|
Training deep neural networks on large and sparse datasets is still
challenging and can require large amounts of computation and memory. In this
work, we address the task of performing semantic segmentation on large
volumetric data sets, such as CT scans. Our contribution is threefold: 1) We
propose a boosted sampling scheme that uses a-posterior error maps, generated
throughout training, to focus sampling on difficult regions, resulting in a
more informative loss. This results in a significant training speed up and
improves learning performance for image segmentation. 2) We propose a novel
algorithm for boosting the SGD learning rate schedule by adaptively increasing
and lowering the learning rate, avoiding the need for extensive hyperparameter
tuning. 3) We show that our method is able to attain new state-of-the-art
results on the VISCERAL Anatomy benchmark.
| null |
http://arxiv.org/abs/1806.05974v2
|
http://arxiv.org/pdf/1806.05974v2.pdf
| null |
[
"Lorenz Berger",
"Eoin Hyde",
"Matt Gibb",
"Nevil Pavithran",
"Garin Kelly",
"Faiz Mumtaz",
"Sébastien Ourselin"
] |
[
"Anatomy",
"Image Segmentation",
"Segmentation",
"Semantic Segmentation"
] | 2018-06-13T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/4e0ac120e9a8b096069c2f892488d630a5c8f358/torch/optim/sgd.py#L97-L112",
"description": "**Stochastic Gradient Descent** is an iterative optimization technique that uses minibatches of data to form an expectation of the gradient, rather than the full gradient using all available data. That is for weights $w$ and a loss function $L$ we have:\r\n\r\n$$ w\\_{t+1} = w\\_{t} - \\eta\\hat{\\nabla}\\_{w}{L(w\\_{t})} $$\r\n\r\nWhere $\\eta$ is a learning rate. SGD reduces redundancy compared to batch gradient descent - which recomputes gradients for similar examples before each parameter update - so it is usually much faster.\r\n\r\n(Image Source: [here](http://rasbt.github.io/mlxtend/user_guide/general_concepts/gradient-optimization/))",
"full_name": "Stochastic Gradient Descent",
"introduced_year": 1951,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "SGD",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/manifold-mixup-better-representations-by
|
1806.05236
| null | null |
Manifold Mixup: Better Representations by Interpolating Hidden States
|
Deep neural networks excel at learning the training data, but often provide incorrect and confident predictions when evaluated on slightly different test examples. This includes distribution shifts, outliers, and adversarial examples. To address these issues, we propose Manifold Mixup, a simple regularizer that encourages neural networks to predict less confidently on interpolations of hidden representations. Manifold Mixup leverages semantic interpolations as additional training signal, obtaining neural networks with smoother decision boundaries at multiple levels of representation. As a result, neural networks trained with Manifold Mixup learn class-representations with fewer directions of variance. We prove theory on why this flattening happens under ideal conditions, validate it on practical situations, and connect it to previous works on information theory and generalization. In spite of incurring no significant computation and being implemented in a few lines of code, Manifold Mixup improves strong baselines in supervised learning, robustness to single-step adversarial attacks, and test log-likelihood.
|
Deep neural networks excel at learning the training data, but often provide incorrect and confident predictions when evaluated on slightly different test examples.
|
https://arxiv.org/abs/1806.05236v7
|
https://arxiv.org/pdf/1806.05236v7.pdf
|
ICLR 2019 5
|
[
"Vikas Verma",
"Alex Lamb",
"Christopher Beckham",
"Amir Najafi",
"Ioannis Mitliagkas",
"Aaron Courville",
"David Lopez-Paz",
"Yoshua Bengio"
] |
[
"Image Classification"
] | 2018-06-13T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/vikasverma1077/manifold_mixup/blob/118ec709808b79dd336b10f4cf7deeacf541dfc7/supervised/models/resnet.py#L98",
"description": "**Manifold Mixup** is a regularization method that encourages neural networks to predict less confidently on interpolations of hidden representations. It leverages semantic interpolations as an additional training signal, obtaining neural networks with smoother decision boundaries at multiple levels of representation. As a result, neural networks trained with Manifold Mixup learn class-representations with fewer directions of variance.\r\n\r\nConsider training a deep neural network $f\\left(x\\right) = f\\_{k}\\left(g\\_{k}\\left(x\\right)\\right)$, where $g\\_{k}$ denotes the part of the neural network mapping the input data to the hidden representation at layer $k$, and $f\\_{k}$ denotes the\r\npart mapping such hidden representation to the output $f\\left(x\\right)$. Training $f$ using Manifold Mixup is performed in five steps:\r\n\r\n(1) Select a random layer $k$ from a set of eligible layers $S$ in the neural network. This set may include the input layer $g\\_{0}\\left(x\\right)$.\r\n\r\n(2) Process two random data minibatches $\\left(x, y\\right)$ and $\\left(x', y'\\right)$ as usual, until reaching layer $k$. This provides us with two intermediate minibatches $\\left(g\\_{k}\\left(x\\right), y\\right)$ and $\\left(g\\_{k}\\left(x'\\right), y'\\right)$.\r\n\r\n(3) Perform Input [Mixup](https://paperswithcode.com/method/mixup) on these intermediate minibatches. This produces the mixed minibatch:\r\n\r\n$$\r\n\\left(\\tilde{g}\\_{k}, \\tilde{y}\\right) = \\left(\\text{Mix}\\_{\\lambda}\\left(g\\_{k}\\left(x\\right), g\\_{k}\\left(x'\\right)\\right), \\text{Mix}\\_{\\lambda}\\left(y, y'\\right\r\n)\\right),\r\n$$\r\n\r\nwhere $\\text{Mix}\\_{\\lambda}\\left(a, b\\right) = \\lambda \\cdot a + \\left(1 − \\lambda\\right) \\cdot b$. Here, $\\left(y, y'\r\n\\right)$ are one-hot labels, and the mixing coefficient\r\n$\\lambda \\sim \\text{Beta}\\left(\\alpha, \\alpha\\right)$ as in mixup. For instance, $\\alpha = 1.0$ is equivalent to sampling $\\lambda \\sim U\\left(0, 1\\right)$.\r\n\r\n(4) Continue the forward pass in the network from layer $k$ until the output using the mixed minibatch $\\left(\\tilde{g}\\_{k}, \\tilde{y}\\right)$.\r\n\r\n(5) This output is used to compute the loss value and\r\ngradients that update all the parameters of the neural network.",
"full_name": "Manifold Mixup",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Manifold Mixup",
"source_title": "Manifold Mixup: Better Representations by Interpolating Hidden States",
"source_url": "https://arxiv.org/abs/1806.05236v7"
},
{
"code_snippet_url": "https://github.com/facebookresearch/mixup-cifar10",
"description": "**Mixup** is a data augmentation technique that generates a weighted combination of random image pairs from the training data. Given two images and their ground truth labels: $\\left(x\\_{i}, y\\_{i}\\right), \\left(x\\_{j}, y\\_{j}\\right)$, a synthetic training example $\\left(\\hat{x}, \\hat{y}\\right)$ is generated as:\r\n\r\n$$ \\hat{x} = \\lambda{x\\_{i}} + \\left(1 − \\lambda\\right){x\\_{j}} $$\r\n$$ \\hat{y} = \\lambda{y\\_{i}} + \\left(1 − \\lambda\\right){y\\_{j}} $$\r\n\r\nwhere $\\lambda \\sim \\text{Beta}\\left(\\alpha = 0.2\\right)$ is independently sampled for each augmented example.",
"full_name": "Mixup",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Data Augmentation** refers to a class of methods that augment an image dataset to increase the effective size of the training set, or as a form of regularization to help the network learn more effective representations.",
"name": "Image Data Augmentation",
"parent": null
},
"name": "Mixup",
"source_title": "mixup: Beyond Empirical Risk Minimization",
"source_url": "http://arxiv.org/abs/1710.09412v2"
}
] |
https://paperswithcode.com/paper/understanding-the-meaning-of-understanding
|
1806.05234
| null | null |
Understanding the Meaning of Understanding
|
Can we train a machine to detect if another machine has understood a concept?
In principle, this is possible by conducting tests on the subject of that
concept. However we want this procedure to be done by avoiding direct
questions. In other words, we would like to isolate the absolute meaning of an
abstract idea by putting it into a class of equivalence, hence without adopting
straight definitions or showing how this idea "works" in practice. We discuss
the metaphysical implications hidden in the above question, with the aim of
providing a plausible reference framework.
| null |
http://arxiv.org/abs/1806.05234v2
|
http://arxiv.org/pdf/1806.05234v2.pdf
| null |
[
"Daniele Funaro"
] |
[] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/end-to-end-parkinson-disease-diagnosis-using
|
1806.05233
| null | null |
End-to-End Parkinson Disease Diagnosis using Brain MR-Images by 3D-CNN
|
In this work, we use a deep learning framework for simultaneous
classification and regression of Parkinson disease diagnosis based on MR-Images
and personal information (i.e. age, gender). We intend to facilitate and
increase the confidence in Parkinson disease diagnosis through our deep
learning framework.
| null |
http://arxiv.org/abs/1806.05233v1
|
http://arxiv.org/pdf/1806.05233v1.pdf
| null |
[
"Soheil Esmaeilzadeh",
"Yao Yang",
"Ehsan Adeli"
] |
[
"Deep Learning",
"General Classification",
"regression"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/beyond-bags-of-words-inferring-systemic-nets
|
1806.05231
| null | null |
Beyond Bags of Words: Inferring Systemic Nets
|
Textual analytics based on representations of documents as bags of words have
been reasonably successful. However, analysis that requires deeper insight into
language, into author properties, or into the contexts in which documents were
created requires a richer representation. Systemic nets are one such
representation. They have not been extensively used because they required human
effort to construct. We show that systemic nets can be algorithmically inferred
from corpora, that the resulting nets are plausible, and that they can provide
practical benefits for knowledge discovery problems. This opens up a new class
of practical analysis techniques for textual analytics.
| null |
http://arxiv.org/abs/1806.05231v1
|
http://arxiv.org/pdf/1806.05231v1.pdf
| null |
[
"D. B. Skillicorn",
"N. Alsadhan"
] |
[] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/identifying-recurring-patterns-with-deep
|
1806.05229
| null | null |
Identifying Recurring Patterns with Deep Neural Networks for Natural Image Denoising
|
Image denoising methods must effectively model, implicitly or explicitly, the vast diversity of patterns and textures that occur in natural images. This is challenging, even for modern methods that leverage deep neural networks trained to regress to clean images from noisy inputs. One recourse is to rely on "internal" image statistics, by searching for similar patterns within the input image itself. In this work, we propose a new method for natural image denoising that trains a deep neural network to determine whether patches in a noisy image input share common underlying patterns. Given a pair of noisy patches, our network predicts whether different sub-band coefficients of the original noise-free patches are similar. The denoising algorithm then aggregates matched coefficients to obtain an initial estimate of the clean image. Finally, this estimate is provided as input, along with the original noisy image, to a standard regression-based denoising network. Experiments show that our method achieves state-of-the-art color image denoising performance, including with a blind version that trains a common model for a range of noise levels, and does not require knowledge of level of noise in an input image. Our approach also has a distinct advantage when training with limited amounts of training data.
|
In this work, we propose a new method for natural image denoising that trains a deep neural network to determine whether patches in a noisy image input share common underlying patterns.
|
https://arxiv.org/abs/1806.05229v3
|
https://arxiv.org/pdf/1806.05229v3.pdf
| null |
[
"Zhihao Xia",
"Ayan Chakrabarti"
] |
[
"Color Image Denoising",
"Denoising",
"Image Denoising",
"Image Restoration"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/3d-coded-3d-correspondences-by-deep-1
|
1806.05228
| null | null |
3D-CODED : 3D Correspondences by Deep Deformation
|
We present a new deep learning approach for matching deformable shapes by
introducing {\it Shape Deformation Networks} which jointly encode 3D shapes and
correspondences. This is achieved by factoring the surface representation into
(i) a template, that parameterizes the surface, and (ii) a learnt global
feature vector that parameterizes the transformation of the template into the
input surface. By predicting this feature for a new shape, we implicitly
predict correspondences between this shape and the template. We show that these
correspondences can be improved by an additional step which improves the shape
feature by minimizing the Chamfer distance between the input and transformed
template. We demonstrate that our simple approach improves on state-of-the-art
results on the difficult FAUST-inter challenge, with an average correspondence
error of 2.88cm. We show, on the TOSCA dataset, that our method is robust to
many types of perturbations, and generalizes to non-human shapes. This
robustness allows it to perform well on real unclean, meshes from the the SCAPE
dataset.
|
By predicting this feature for a new shape, we implicitly predict correspondences between this shape and the template.
|
http://arxiv.org/abs/1806.05228v2
|
http://arxiv.org/pdf/1806.05228v2.pdf
| null |
[
"Thibault Groueix",
"Matthew Fisher",
"Vladimir G. Kim",
"Bryan C. Russell",
"Mathieu Aubry"
] |
[
"3D Dense Shape Correspondence",
"3D Human Pose Estimation",
"3D Point Cloud Matching",
"3D Surface Generation"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/human-activity-recognition-based-on-wearable
|
1806.05226
| null | null |
Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art
|
Human activity recognition based on wearable sensor data has been an
attractive research topic due to its application in areas such as healthcare
and smart environments. In this context, many works have presented remarkable
results using accelerometer, gyroscope and magnetometer data to represent the
activities categories. However, current studies do not consider important
issues that lead to skewed results, making it hard to assess the quality of
sensor-based human activity recognition and preventing a direct comparison of
previous works. These issues include the samples generation processes and the
validation protocols used. We emphasize that in other research areas, such as
image classification and object detection, these issues are already
well-defined, which brings more efforts towards the application. Inspired by
this, we conduct an extensive set of experiments that analyze different sample
generation processes and validation protocols to indicate the vulnerable points
in human activity recognition based on wearable sensor data. For this purpose,
we implement and evaluate several top-performance methods, ranging from
handcrafted-based approaches to convolutional neural networks. According to our
study, most of the experimental evaluations that are currently employed are not
adequate to perform the activity recognition in the context of wearable sensor
data, in which the recognition accuracy drops considerably when compared to an
appropriate evaluation approach. To the best of our knowledge, this is the
first study that tackles essential issues that compromise the understanding of
the performance in human activity recognition based on wearable sensor data.
|
Inspired by this, we conduct an extensive set of experiments that analyze different sample generation processes and validation protocols to indicate the vulnerable points in human activity recognition based on wearable sensor data.
|
http://arxiv.org/abs/1806.05226v3
|
http://arxiv.org/pdf/1806.05226v3.pdf
| null |
[
"Artur Jordao",
"Antonio C. Nazare Jr.",
"Jessica Sena",
"William Robson Schwartz"
] |
[
"Activity Recognition",
"Human Activity Recognition",
"image-classification",
"Image Classification",
"object-detection",
"Object Detection"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-spontaneity-to-improve-emotion
|
1712.04753
| null | null |
Learning Spontaneity to Improve Emotion Recognition In Speech
|
We investigate the effect and usefulness of spontaneity (i.e. whether a given
speech is spontaneous or not) in speech in the context of emotion recognition.
We hypothesize that emotional content in speech is interrelated with its
spontaneity, and use spontaneity classification as an auxiliary task to the
problem of emotion recognition. We propose two supervised learning settings
that utilize spontaneity to improve speech emotion recognition: a hierarchical
model that performs spontaneity detection before performing emotion
recognition, and a multitask learning model that jointly learns to recognize
both spontaneity and emotion. Through various experiments on the well known
IEMOCAP database, we show that by using spontaneity detection as an additional
task, significant improvement can be achieved over emotion recognition systems
that are unaware of spontaneity. We achieve state-of-the-art emotion
recognition accuracy (4-class, 69.1%) on the IEMOCAP database outperforming
several relevant and competitive baselines.
| null |
http://arxiv.org/abs/1712.04753v3
|
http://arxiv.org/pdf/1712.04753v3.pdf
| null |
[
"Karttikeya Mangalam",
"Tanaya Guha"
] |
[
"Emotion Recognition",
"Speech Emotion Recognition"
] | 2017-12-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/bringing-replication-and-reproduction
|
1806.05219
| null | null |
Bringing replication and reproduction together with generalisability in NLP: Three reproduction studies for Target Dependent Sentiment Analysis
|
Lack of repeatability and generalisability are two significant threats to
continuing scientific development in Natural Language Processing. Language
models and learning methods are so complex that scientific conference papers no
longer contain enough space for the technical depth required for replication or
reproduction. Taking Target Dependent Sentiment Analysis as a case study, we
show how recent work in the field has not consistently released code, or
described settings for learning methods in enough detail, and lacks
comparability and generalisability in train, test or validation data. To
investigate generalisability and to enable state of the art comparative
evaluations, we carry out the first reproduction studies of three groups of
complementary methods and perform the first large-scale mass evaluation on six
different English datasets. Reflecting on our experiences, we recommend that
future replication or reproduction experiments should always consider a variety
of datasets alongside documenting and releasing their methods and published
code in order to minimise the barriers to both repeatability and
generalisability. We have released our code with a model zoo on GitHub with
Jupyter Notebooks to aid understanding and full documentation, and we recommend
that others do the same with their papers at submission time through an
anonymised GitHub account.
|
Lack of repeatability and generalisability are two significant threats to continuing scientific development in Natural Language Processing.
|
http://arxiv.org/abs/1806.05219v2
|
http://arxiv.org/pdf/1806.05219v2.pdf
|
COLING 2018 8
|
[
"Andrew Moore",
"Paul Rayson"
] |
[
"Sentiment Analysis"
] | 2018-06-13T00:00:00 |
https://aclanthology.org/C18-1097
|
https://aclanthology.org/C18-1097.pdf
|
bringing-replication-and-reproduction-2
| null |
[] |
https://paperswithcode.com/paper/impostor-networks-for-fast-fine-grained
|
1806.05217
| null | null |
Impostor Networks for Fast Fine-Grained Recognition
|
In this work we introduce impostor networks, an architecture that allows to
perform fine-grained recognition with high accuracy and using a light-weight
convolutional network, making it particularly suitable for fine-grained
applications on low-power and non-GPU enabled platforms. Impostor networks
compensate for the lightness of its `backend' network by combining it with a
lightweight non-parametric classifier. The combination of a convolutional
network and such non-parametric classifier is trained in an end-to-end fashion.
Similarly to convolutional neural networks, impostor networks can fit
large-scale training datasets very well, while also being able to generalize to
new data points. At the same time, the bulk of computations within impostor
networks happen through nearest neighbor search in high-dimensions. Such search
can be performed efficiently on a variety of architectures including standard
CPUs, where deep convolutional networks are inefficient. In a series of
experiments with three fine-grained datasets, we show that impostor networks
are able to boost the classification accuracy of a moderate-sized convolutional
network considerably at a very small computational cost.
| null |
http://arxiv.org/abs/1806.05217v1
|
http://arxiv.org/pdf/1806.05217v1.pdf
| null |
[
"Vadim Lebedev",
"Artem Babenko",
"Victor Lempitsky"
] |
[
"GPU"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/an-evaluation-of-neural-machine-translation
|
1806.05210
| null | null |
An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization
|
In this paper, we apply different NMT models to the problem of historical
spelling normalization for five languages: English, German, Hungarian,
Icelandic, and Swedish. The NMT models are at different levels, have different
attention mechanisms, and different neural network architectures. Our results
show that NMT models are much better than SMT models in terms of character
error rate. The vanilla RNNs are competitive to GRUs/LSTMs in historical
spelling normalization. Transformer models perform better only when provided
with more training data. We also find that subword-level models with a small
subword vocabulary are better than character-level models for low-resource
languages. In addition, we propose a hybrid method which further improves the
performance of historical spelling normalization.
|
In this paper, we apply different NMT models to the problem of historical spelling normalization for five languages: English, German, Hungarian, Icelandic, and Swedish.
|
http://arxiv.org/abs/1806.05210v2
|
http://arxiv.org/pdf/1806.05210v2.pdf
|
COLING 2018 8
|
[
"Gongbo Tang",
"Fabienne Cap",
"Eva Pettersson",
"Joakim Nivre"
] |
[
"Machine Translation",
"NMT",
"Translation"
] | 2018-06-13T00:00:00 |
https://aclanthology.org/C18-1112
|
https://aclanthology.org/C18-1112.pdf
|
an-evaluation-of-neural-machine-translation-1
| null |
[
{
"code_snippet_url": null,
"description": "A **Linear Layer** is a projection $\\mathbf{XW + b}$.",
"full_name": "Linear Layer",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Linear Layer",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Absolute Position Encodings** are a type of position embeddings for [[Transformer](https://paperswithcode.com/method/transformer)-based models] where positional encodings are added to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension $d\\_{model}$ as the embeddings, so that the two can be summed. In the original implementation, sine and cosine functions of different frequencies are used:\r\n\r\n$$ \\text{PE}\\left(pos, 2i\\right) = \\sin\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\n$$ \\text{PE}\\left(pos, 2i+1\\right) = \\cos\\left(pos/10000^{2i/d\\_{model}}\\right) $$\r\n\r\nwhere $pos$ is the position and $i$ is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from $2\\pi$ to $10000 \\dot 2\\pi$. This function was chosen because the authors hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $\\text{PE}\\_{pos+k}$ can be represented as a linear function of $\\text{PE}\\_{pos}$.\r\n\r\nImage Source: [D2L.ai](https://d2l.ai/chapter_attention-mechanisms/self-attention-and-positional-encoding.html)",
"full_name": "Absolute Position Encodings",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Position Embeddings",
"parent": null
},
"name": "Absolute Position Encodings",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": null,
"description": "**Position-Wise Feed-Forward Layer** is a type of [feedforward layer](https://www.paperswithcode.com/method/category/feedforwad-networks) consisting of two [dense layers](https://www.paperswithcode.com/method/dense-connections) that applies to the last dimension, which means the same dense layers are used for each position item in the sequence, so called position-wise.",
"full_name": "Position-Wise Feed-Forward Layer",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Position-Wise Feed-Forward Layer",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": null,
"description": "**Byte Pair Encoding**, or **BPE**, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations).\r\n\r\n[Lei Mao](https://leimao.github.io/blog/Byte-Pair-Encoding/) has a detailed blog post that explains how this works.",
"full_name": "Byte Pair Encoding",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Subword Segmentation",
"parent": null
},
"name": "BPE",
"source_title": "Neural Machine Translation of Rare Words with Subword Units",
"source_url": "http://arxiv.org/abs/1508.07909v5"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Label Smoothing** is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the likelihood of $\\log{p}\\left(y\\mid{x}\\right)$ directly can be harmful. Assume for a small constant $\\epsilon$, the training set label $y$ is correct with probability $1-\\epsilon$ and incorrect otherwise. Label Smoothing regularizes a model based on a [softmax](https://paperswithcode.com/method/softmax) with $k$ output values by replacing the hard $0$ and $1$ classification targets with targets of $\\frac{\\epsilon}{k}$ and $1-\\frac{k-1}{k}\\epsilon$ respectively.\r\n\r\nSource: Deep Learning, Goodfellow et al\r\n\r\nImage Source: [When Does Label Smoothing Help?](https://arxiv.org/abs/1906.02629)",
"full_name": "Label Smoothing",
"introduced_year": 1985,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Label Smoothing",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.com/method/rmsprop) and [SGD w/th Momentum](https://paperswithcode.com/method/sgd-with-momentum). The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. \r\n\r\nThe weight updates are performed as:\r\n\r\n$$ w_{t} = w_{t-1} - \\eta\\frac{\\hat{m}\\_{t}}{\\sqrt{\\hat{v}\\_{t}} + \\epsilon} $$\r\n\r\nwith\r\n\r\n$$ \\hat{m}\\_{t} = \\frac{m_{t}}{1-\\beta^{t}_{1}} $$\r\n\r\n$$ \\hat{v}\\_{t} = \\frac{v_{t}}{1-\\beta^{t}_{2}} $$\r\n\r\n$$ m_{t} = \\beta_{1}m_{t-1} + (1-\\beta_{1})g_{t} $$\r\n\r\n$$ v_{t} = \\beta_{2}v_{t-1} + (1-\\beta_{2})g_{t}^{2} $$\r\n\r\n\r\n$ \\eta $ is the step size/learning rate, around 1e-3 in the original paper. $ \\epsilon $ is a small number, typically 1e-8 or 1e-10, to prevent dividing by zero. $ \\beta_{1} $ and $ \\beta_{2} $ are forgetting parameters, with typical values 0.9 and 0.999, respectively.",
"full_name": "Adam",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "Adam",
"source_title": "Adam: A Method for Stochastic Optimization",
"source_url": "http://arxiv.org/abs/1412.6980v9"
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/fec78a687210851f055f792d45300d27cc60ae41/transformer/SubLayers.py#L9",
"description": "**Multi-head Attention** is a module for attention mechanisms which runs through an attention mechanism several times in parallel. The independent attention outputs are then concatenated and linearly transformed into the expected dimension. Intuitively, multiple attention heads allows for attending to parts of the sequence differently (e.g. longer-term dependencies versus shorter-term dependencies). \r\n\r\n$$ \\text{MultiHead}\\left(\\textbf{Q}, \\textbf{K}, \\textbf{V}\\right) = \\left[\\text{head}\\_{1},\\dots,\\text{head}\\_{h}\\right]\\textbf{W}_{0}$$\r\n\r\n$$\\text{where} \\text{ head}\\_{i} = \\text{Attention} \\left(\\textbf{Q}\\textbf{W}\\_{i}^{Q}, \\textbf{K}\\textbf{W}\\_{i}^{K}, \\textbf{V}\\textbf{W}\\_{i}^{V} \\right) $$\r\n\r\nAbove $\\textbf{W}$ are all learnable parameter matrices.\r\n\r\nNote that [scaled dot-product attention](https://paperswithcode.com/method/scaled) is most commonly used in this module, although in principle it can be swapped out for other types of attention mechanism.\r\n\r\nSource: [Lilian Weng](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html#a-family-of-attention-mechanisms)",
"full_name": "Multi-Head Attention",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Modules** refer to modules that incorporate attention mechanisms. For example, multi-head attention is a module that incorporates multiple attention heads. Below you can find a continuously updating list of attention modules.",
"name": "Attention Modules",
"parent": "Attention"
},
"name": "Multi-Head Attention",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** directly estimates the normalization statistics from the summed inputs to the neurons within a hidden layer so the normalization does not introduce any new dependencies between training cases. It works well for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and improves both the training time and the generalization performance of several existing RNN models. More recently, it has been used with [Transformer](https://paperswithcode.com/methods/category/transformers) models.\r\n\r\nWe compute the layer normalization statistics over all the hidden units in the same layer as follows:\r\n\r\n$$ \\mu^{l} = \\frac{1}{H}\\sum^{H}\\_{i=1}a\\_{i}^{l} $$\r\n\r\n$$ \\sigma^{l} = \\sqrt{\\frac{1}{H}\\sum^{H}\\_{i=1}\\left(a\\_{i}^{l}-\\mu^{l}\\right)^{2}} $$\r\n\r\nwhere $H$ denotes the number of hidden units in a layer. Under layer normalization, all the hidden units in a layer share the same normalization terms $\\mu$ and $\\sigma$, but different training cases have different normalization terms. Unlike batch normalization, layer normalization does not impose any constraint on the size of the mini-batch and it can be used in the pure online regime with batch size 1.",
"full_name": "Layer Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Layer Normalization",
"source_title": "Layer Normalization",
"source_url": "http://arxiv.org/abs/1607.06450v1"
},
{
"code_snippet_url": "",
"description": "",
"full_name": "Attention Is All You Need",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "If you're looking to get in touch with American Airlines fast, ☎️+1-801-(855)-(5905)or +1-804-853-9001✅ there are\r\nseveral efficient ways to reach their customer service team. The quickest method is to dial ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. American’s phone service ensures that you can speak with a live\r\nrepresentative promptly to resolve any issues or queries regarding your booking, reservation,\r\nor any changes, such as name corrections or ticket cancellations.",
"name": "Attention Mechanisms",
"parent": "Attention"
},
"name": "Attention",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
},
{
"code_snippet_url": "https://github.com/tunz/transformer-pytorch/blob/e7266679f0b32fd99135ea617213f986ceede056/model/transformer.py#L201",
"description": "A **Transformer** is a model architecture that eschews recurrence and instead relies entirely on an [attention mechanism](https://paperswithcode.com/methods/category/attention-mechanisms-1) to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favor of [attention mechanisms](https://paperswithcode.com/methods/category/attention-mechanisms-1) allows for significantly more parallelization than methods like [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) and [CNNs](https://paperswithcode.com/methods/category/convolutional-neural-networks).",
"full_name": "Transformer",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Transformers** are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.",
"name": "Transformers",
"parent": "Language Models"
},
"name": "Transformer",
"source_title": "Attention Is All You Need",
"source_url": "https://arxiv.org/abs/1706.03762v7"
}
] |
https://paperswithcode.com/paper/offline-evaluation-of-ranking-policies-with
|
1804.10488
| null | null |
Offline Evaluation of Ranking Policies with Click Models
|
Many web systems rank and present a list of items to users, from recommender
systems to search and advertising. An important problem in practice is to
evaluate new ranking policies offline and optimize them before they are
deployed. We address this problem by proposing evaluation algorithms for
estimating the expected number of clicks on ranked lists from historical logged
data. The existing algorithms are not guaranteed to be statistically efficient
in our problem because the number of recommended lists can grow exponentially
with their length. To overcome this challenge, we use models of user
interaction with the list of items, the so-called click models, to construct
estimators that learn statistically efficiently. We analyze our estimators and
prove that they are more efficient than the estimators that do not use the
structure of the click model, under the assumption that the click model holds.
We evaluate our estimators in a series of experiments on a real-world dataset
and show that they consistently outperform prior estimators.
| null |
http://arxiv.org/abs/1804.10488v2
|
http://arxiv.org/pdf/1804.10488v2.pdf
| null |
[
"Shuai Li",
"Yasin Abbasi-Yadkori",
"Branislav Kveton",
"S. Muthukrishnan",
"Vishwa Vinay",
"Zheng Wen"
] |
[
"Recommendation Systems"
] | 2018-04-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/skeletracks-automatic-separation-of
|
1806.05199
| null | null |
Skeletracks: automatic separation of overlapping fission tracks in apatite and muscovite using image processing
|
One of the major difficulties of automatic track counting using
photomicrographs is separating overlapped tracks. We address this issue
combining image processing algorithms such as skeletonization, and we test our
algorithm with several binarization techniques. The counting algorithm was
successfully applied to determine the efficiency factor GQR, necessary for
standardless fission-track dating, involving counting induced tracks in apatite
and muscovite with superficial densities of about $6 \times 10^5$
tracks/$cm^2$.
|
One of the major difficulties of automatic track counting using photomicrographs is separating overlapped tracks.
|
http://arxiv.org/abs/1806.05199v2
|
http://arxiv.org/pdf/1806.05199v2.pdf
| null |
[
"Alexandre Fioravante de Siqueira",
"Wagner Massayuki Nakasuga",
"Sandro Guedes"
] |
[
"Binarization"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/interpretable-machine-learning-for-privacy
|
1710.08464
| null | null |
Interpretable Machine Learning for Privacy-Preserving Pervasive Systems
|
Our everyday interactions with pervasive systems generate traces that capture various aspects of human behavior and enable machine learning algorithms to extract latent information about users. In this paper, we propose a machine learning interpretability framework that enables users to understand how these generated traces violate their privacy.
| null |
https://arxiv.org/abs/1710.08464v6
|
https://arxiv.org/pdf/1710.08464v6.pdf
| null |
[
"Benjamin Baron",
"Mirco Musolesi"
] |
[
"BIG-bench Machine Learning",
"Interpretable Machine Learning",
"Privacy Preserving"
] | 2017-10-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Please enter a description about the method here",
"full_name": "Interpretability",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Models** are methods that build representations of images for downstream tasks such as classification and object detection. The most popular subcategory are convolutional neural networks. Below you can find a continuously updated list of image models.",
"name": "Image Models",
"parent": null
},
"name": "Interpretability",
"source_title": "CAM: Causal additive models, high-dimensional order search and penalized regression",
"source_url": "http://arxiv.org/abs/1310.1533v2"
}
] |
https://paperswithcode.com/paper/overfitting-or-perfect-fitting-risk-bounds
|
1806.05161
| null | null |
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate
|
Many modern machine learning models are trained to achieve zero or near-zero
training error in order to obtain near-optimal (but non-zero) test error. This
phenomenon of strong generalization performance for "overfitted" / interpolated
classifiers appears to be ubiquitous in high-dimensional data, having been
observed in deep networks, kernel machines, boosting and random forests. Their
performance is consistently robust even when the data contain large amounts of
label noise.
Very little theory is available to explain these observations. The vast
majority of theoretical analyses of generalization allows for interpolation
only when there is little or no label noise. This paper takes a step toward a
theoretical foundation for interpolated classifiers by analyzing local
interpolating schemes, including geometric simplicial interpolation algorithm
and singularly weighted $k$-nearest neighbor schemes. Consistency or
near-consistency is proved for these schemes in classification and regression
problems. Moreover, the nearest neighbor schemes exhibit optimal rates under
some standard statistical assumptions.
Finally, this paper suggests a way to explain the phenomenon of adversarial
examples, which are seemingly ubiquitous in modern machine learning, and also
discusses some connections to kernel machines and random forests in the
interpolated regime.
| null |
http://arxiv.org/abs/1806.05161v3
|
http://arxiv.org/pdf/1806.05161v3.pdf
|
NeurIPS 2018 12
|
[
"Mikhail Belkin",
"Daniel Hsu",
"Partha Mitra"
] |
[
"BIG-bench Machine Learning",
"General Classification",
"regression"
] | 2018-06-13T00:00:00 |
http://papers.nips.cc/paper/7498-overfitting-or-perfect-fitting-risk-bounds-for-classification-and-regression-rules-that-interpolate
|
http://papers.nips.cc/paper/7498-overfitting-or-perfect-fitting-risk-bounds-for-classification-and-regression-rules-that-interpolate.pdf
|
overfitting-or-perfect-fitting-risk-bounds-1
| null |
[] |
https://paperswithcode.com/paper/on-tighter-generalization-bound-for-deep
|
1806.05159
| null | null |
On Tighter Generalization Bound for Deep Neural Networks: CNNs, ResNets, and Beyond
|
We establish a margin based data dependent generalization error bound for a general family of deep neural networks in terms of the depth and width, as well as the Jacobian of the networks. Through introducing a new characterization of the Lipschitz properties of neural network family, we achieve significantly tighter generalization bounds than existing results. Moreover, we show that the generalization bound can be further improved for bounded losses. Aside from the general feedforward deep neural networks, our results can be applied to derive new bounds for popular architectures, including convolutional neural networks (CNNs) and residual networks (ResNets). When achieving same generalization errors with previous arts, our bounds allow for the choice of larger parameter spaces of weight matrices, inducing potentially stronger expressive ability for neural networks. Numerical evaluation is also provided to support our theory.
| null |
https://arxiv.org/abs/1806.05159v4
|
https://arxiv.org/pdf/1806.05159v4.pdf
| null |
[
"Xingguo Li",
"Junwei Lu",
"Zhaoran Wang",
"Jarvis Haupt",
"Tuo Zhao"
] |
[
"Generalization Bounds"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/automated-performance-assessment-in
|
1806.05154
| null | null |
Automated Performance Assessment in Transoesophageal Echocardiography with Convolutional Neural Networks
|
Transoesophageal echocardiography (TEE) is a valuable diagnostic and
monitoring imaging modality. Proper image acquisition is essential for
diagnosis, yet current assessment techniques are solely based on manual expert
review. This paper presents a supervised deep learn ing framework for
automatically evaluating and grading the quality of TEE images. To obtain the
necessary dataset, 38 participants of varied experience performed TEE exams
with a high-fidelity virtual reality (VR) platform. Two Convolutional Neural
Network (CNN) architectures, AlexNet and VGG, structured to perform regression,
were finetuned and validated on manually graded images from three evaluators.
Two different scoring strategies, a criteria-based percentage and an overall
general impression, were used. The developed CNN models estimate the average
score with a root mean square accuracy ranging between 84%-93%, indicating the
ability to replicate expert valuation. Proposed strategies for automated TEE
assessment can have a significant impact on the training process of new TEE
operators, providing direct feedback and facilitating the development of the
necessary dexterous skills.
| null |
http://arxiv.org/abs/1806.05154v1
|
http://arxiv.org/pdf/1806.05154v1.pdf
| null |
[
"Evangelos B. Mazomenos",
"Kamakshi Bansal",
"Bruce Martin",
"Andrew Smith",
"Susan Wright",
"Danail Stoyanov"
] |
[
"Diagnostic"
] | 2018-06-13T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "A **1 x 1 Convolution** is a [convolution](https://paperswithcode.com/method/convolution) with some special properties in that it can be used for dimensionality reduction, efficient low dimensional embeddings, and applying non-linearity after convolutions. It maps an input pixel with all its channels to an output pixel which can be squeezed to a desired output depth. It can be viewed as an [MLP](https://paperswithcode.com/method/feedforward-network) looking at a particular pixel location.\r\n\r\nImage Credit: [http://deeplearning.ai](http://deeplearning.ai)",
"full_name": "1x1 Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "1x1 Convolution",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"code_snippet_url": "",
"description": "In today’s digital age, Ethereum has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Ethereum transaction not confirmed, your Ethereum wallet not showing balance, or you're trying to recover a lost Ethereum wallet, knowing where to get help is essential. That’s why the Ethereum customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Ethereum Customer Support Number +1-833-534-1729\r\nEthereum operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Ethereum Transaction Not Confirmed\r\nOne of the most common concerns is when a Ethereum transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Ethereum Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Ethereum wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Ethereum Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Ethereum wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Ethereum Deposit Not Received\r\nIf someone has sent you Ethereum but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Ethereum deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Ethereum Transaction Stuck or Pending\r\nSometimes your Ethereum transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Ethereum Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Ethereum wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Ethereum Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Ethereum tech.\r\n\r\n24/7 Availability: Ethereum doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Ethereum Support and Wallet Issues\r\nQ1: Can Ethereum support help me recover stolen BTC?\r\nA: While Ethereum transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Ethereum transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Ethereum’s official number (Ethereum is decentralized), it connects you to trained professionals experienced in resolving all major Ethereum issues.\r\n\r\nFinal Thoughts\r\nEthereum is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Ethereum transaction not confirmed, your Ethereum wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Ethereum customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Ethereum Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "If you have questions or want to make special travel arrangements, you can make them online or call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. For hearing or speech impaired assistance dial 711 to be connected through the National Relay Service.",
"name": "Convolutional Neural Networks",
"parent": "Image Models"
},
"name": "Ethereum Customer Service Number +1-833-534-1729",
"source_title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
"source_url": "http://arxiv.org/abs/1409.1556v6"
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/1c5c289b6218eb1026dcb5fd9738231401cfccea/torch/nn/modules/normalization.py#L13",
"description": "**Local Response Normalization** is a normalization layer that implements the idea of lateral inhibition. Lateral inhibition is a concept in neurobiology that refers to the phenomenon of an excited neuron inhibiting its neighbours: this leads to a peak in the form of a local maximum, creating contrast in that area and increasing sensory perception. In practice, we can either normalize within the same channel or normalize across channels when we apply LRN to convolutional neural networks.\r\n\r\n$$ b_{c} = a_{c}\\left(k + \\frac{\\alpha}{n}\\sum_{c'=\\max(0, c-n/2)}^{\\min(N-1,c+n/2)}a_{c'}^2\\right)^{-\\beta} $$\r\n\r\nWhere the size is the number of neighbouring channels used for normalization, $\\alpha$ is multiplicative factor, $\\beta$ an exponent and $k$ an additive factor",
"full_name": "Local Response Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Local Response Normalization",
"source_title": "ImageNet Classification with Deep Convolutional Neural Networks",
"source_url": "http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks"
},
{
"code_snippet_url": "https://github.com/prlz77/ResNeXt.pytorch/blob/39fb8d03847f26ec02fb9b880ecaaa88db7a7d16/models/model.py#L42",
"description": "A **Grouped Convolution** uses a group of convolutions - multiple kernels per layer - resulting in multiple channel outputs per layer. This leads to wider networks helping a network learn a varied set of low level and high level features. The original motivation of using Grouped Convolutions in [AlexNet](https://paperswithcode.com/method/alexnet) was to distribute the model over multiple GPUs as an engineering compromise. But later, with models such as [ResNeXt](https://paperswithcode.com/method/resnext), it was shown this module could be used to improve classification accuracy. Specifically by exposing a new dimension through grouped convolutions, *cardinality* (the size of set of transformations), we can increase accuracy by increasing it.",
"full_name": "Grouped Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Grouped Convolution",
"source_title": "ImageNet Classification with Deep Convolutional Neural Networks",
"source_url": "http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks"
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Max Pooling** is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs.\r\n\r\nImage Source: [here](https://computersciencewiki.org/index.php/File:MaxpoolSample2.png)",
"full_name": "Max Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Max Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/dansuh17/alexnet-pytorch/blob/d0c1b1c52296ffcbecfbf5b17e1d1685b4ca6744/model.py#L40",
"description": "To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.ggfdf\r\n\r\n\r\nHow do I speak to a person at Expedia?How do I speak to a person at Expedia?To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.\r\n\r\n\r\n\r\nTo make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.chgd",
"full_name": "How do I speak to a person at Expedia?-/+/",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "If you have questions or want to make special travel arrangements, you can make them online or call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. For hearing or speech impaired assistance dial 711 to be connected through the National Relay Service.",
"name": "Convolutional Neural Networks",
"parent": "Image Models"
},
"name": "How do I speak to a person at Expedia?-/+/",
"source_title": "ImageNet Classification with Deep Convolutional Neural Networks",
"source_url": "http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks"
}
] |
https://paperswithcode.com/paper/bandits-with-delayed-aggregated-anonymous
|
1709.06853
| null | null |
Bandits with Delayed, Aggregated Anonymous Feedback
|
We study a variant of the stochastic $K$-armed bandit problem, which we call
"bandits with delayed, aggregated anonymous feedback". In this problem, when
the player pulls an arm, a reward is generated, however it is not immediately
observed. Instead, at the end of each round the player observes only the sum of
a number of previously generated rewards which happen to arrive in the given
round. The rewards are stochastically delayed and due to the aggregated nature
of the observations, the information of which arm led to a particular reward is
lost. The question is what is the cost of the information loss due to this
delayed, aggregated anonymous feedback? Previous works have studied bandits
with stochastic, non-anonymous delays and found that the regret increases only
by an additive factor relating to the expected delay. In this paper, we show
that this additive regret increase can be maintained in the harder delayed,
aggregated anonymous feedback setting when the expected delay (or a bound on
it) is known. We provide an algorithm that matches the worst case regret of the
non-anonymous problem exactly when the delays are bounded, and up to
logarithmic factors or an additive variance term for unbounded delays.
| null |
http://arxiv.org/abs/1709.06853v3
|
http://arxiv.org/pdf/1709.06853v3.pdf
|
ICML 2018 7
|
[
"Ciara Pike-Burke",
"Shipra Agrawal",
"Csaba Szepesvari",
"Steffen Grunewalder"
] |
[] | 2017-09-20T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2212
|
http://proceedings.mlr.press/v80/pike-burke18a/pike-burke18a.pdf
|
bandits-with-delayed-aggregated-anonymous-1
| null |
[] |
https://paperswithcode.com/paper/on-landscape-of-lagrangian-functions-and
|
1806.05151
| null | null |
On Landscape of Lagrangian Functions and Stochastic Search for Constrained Nonconvex Optimization
|
We study constrained nonconvex optimization problems in machine learning, signal processing, and stochastic control. It is well-known that these problems can be rewritten to a minimax problem in a Lagrangian form. However, due to the lack of convexity, their landscape is not well understood and how to find the stable equilibria of the Lagrangian function is still unknown. To bridge the gap, we study the landscape of the Lagrangian function. Further, we define a special class of Lagrangian functions. They enjoy two properties: 1.Equilibria are either stable or unstable (Formal definition in Section 2); 2.Stable equilibria correspond to the global optima of the original problem. We show that a generalized eigenvalue (GEV) problem, including canonical correlation analysis and other problems, belongs to the class. Specifically, we characterize its stable and unstable equilibria by leveraging an invariant group and symmetric property (more details in Section 3). Motivated by these neat geometric structures, we propose a simple, efficient, and stochastic primal-dual algorithm solving the online GEV problem. Theoretically, we provide sufficient conditions, based on which we establish an asymptotic convergence rate and obtain the first sample complexity result for the online GEV problem by diffusion approximations, which are widely used in applied probability and stochastic control. Numerical results are provided to support our theory.
| null |
https://arxiv.org/abs/1806.05151v3
|
https://arxiv.org/pdf/1806.05151v3.pdf
| null |
[
"Zhehui Chen",
"Xingguo Li",
"Lin F. Yang",
"Jarvis Haupt",
"Tuo Zhao"
] |
[] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/3d-convolutional-neural-networks-for
|
1806.04209
| null | null |
3D Convolutional Neural Networks for Classification of Functional Connectomes
|
Resting-state functional MRI (rs-fMRI) scans hold the potential to serve as a
diagnostic or prognostic tool for a wide variety of conditions, such as autism,
Alzheimer's disease, and stroke. While a growing number of studies have
demonstrated the promise of machine learning algorithms for rs-fMRI based
clinical or behavioral prediction, most prior models have been limited in their
capacity to exploit the richness of the data. For example, classification
techniques applied to rs-fMRI often rely on region-based summary statistics
and/or linear models. In this work, we propose a novel volumetric Convolutional
Neural Network (CNN) framework that takes advantage of the full-resolution 3D
spatial structure of rs-fMRI data and fits non-linear predictive models. We
showcase our approach on a challenging large-scale dataset (ABIDE, with N >
2,000) and report state-of-the-art accuracy results on rs-fMRI-based
discrimination of autism patients and healthy controls.
| null |
http://arxiv.org/abs/1806.04209v2
|
http://arxiv.org/pdf/1806.04209v2.pdf
| null |
[
"Meenakshi Khosla",
"Keith Jamison",
"Amy Kuceyeski",
"Mert Sabuncu"
] |
[
"Classification",
"Diagnostic",
"General Classification"
] | 2018-06-11T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/presentation-attack-detection-for-iris
|
1804.00194
| null | null |
Presentation Attack Detection for Iris Recognition: An Assessment of the State of the Art
|
Iris recognition is increasingly used in large-scale applications. As a
result, presentation attack detection for iris recognition takes on fundamental
importance. This survey covers the diverse research literature on this topic.
Different categories of presentation attack are described and placed in an
application-relevant framework, and the state of the art in detecting each
category of attack is summarized. One conclusion from this is that presentation
attack detection for iris recognition is not yet a solved problem. Datasets
available for research are described, research directions for the near- and
medium-term future are outlined, and a short list of recommended readings are
suggested.
| null |
http://arxiv.org/abs/1804.00194v3
|
http://arxiv.org/pdf/1804.00194v3.pdf
| null |
[
"Adam Czajka",
"Kevin W. Bowyer"
] |
[
"Iris Recognition"
] | 2018-03-31T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/active-learning-with-logged-data
|
1802.09069
| null | null |
Active Learning with Logged Data
|
We consider active learning with logged data, where labeled examples are
drawn conditioned on a predetermined logging policy, and the goal is to learn a
classifier on the entire population, not just conditioned on the logging
policy. Prior work addresses this problem either when only logged data is
available, or purely in a controlled random experimentation setting where the
logged data is ignored. In this work, we combine both approaches to provide an
algorithm that uses logged data to bootstrap and inform experimentation, thus
achieving the best of both worlds. Our work is inspired by a connection between
controlled random experimentation and active learning, and modifies existing
disagreement-based active learning algorithms to exploit logged data.
| null |
http://arxiv.org/abs/1802.09069v3
|
http://arxiv.org/pdf/1802.09069v3.pdf
|
ICML 2018 7
|
[
"Songbai Yan",
"Kamalika Chaudhuri",
"Tara Javidi"
] |
[
"Active Learning"
] | 2018-02-25T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=1990
|
http://proceedings.mlr.press/v80/yan18a/yan18a.pdf
|
active-learning-with-logged-data-1
| null |
[] |
https://paperswithcode.com/paper/lagrange-coded-computing-optimal-design-for
|
1806.00939
| null | null |
Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy
|
We consider a scenario involving computations over a massive dataset stored
distributedly across multiple workers, which is at the core of distributed
learning algorithms. We propose Lagrange Coded Computing (LCC), a new framework
to simultaneously provide (1) resiliency against stragglers that may prolong
computations; (2) security against Byzantine (or malicious) workers that
deliberately modify the computation for their benefit; and (3)
(information-theoretic) privacy of the dataset amidst possible collusion of
workers. LCC, which leverages the well-known Lagrange polynomial to create
computation redundancy in a novel coded form across workers, can be applied to
any computation scenario in which the function of interest is an arbitrary
multivariate polynomial of the input dataset, hence covering many computations
of interest in machine learning. LCC significantly generalizes prior works to
go beyond linear computations. It also enables secure and private computing in
distributed settings, improving the computation and communication efficiency of
the state-of-the-art. Furthermore, we prove the optimality of LCC by showing
that it achieves the optimal tradeoff between resiliency, security, and
privacy, i.e., in terms of tolerating the maximum number of stragglers and
adversaries, and providing data privacy against the maximum number of colluding
workers. Finally, we show via experiments on Amazon EC2 that LCC speeds up the
conventional uncoded implementation of distributed least-squares linear
regression by up to $13.43\times$, and also achieves a
$2.36\times$-$12.65\times$ speedup over the state-of-the-art straggler
mitigation strategies.
| null |
http://arxiv.org/abs/1806.00939v4
|
http://arxiv.org/pdf/1806.00939v4.pdf
| null |
[
"Qian Yu",
"Songze Li",
"Netanel Raviv",
"Seyed Mohammadreza Mousavi Kalan",
"Mahdi Soltanolkotabi",
"Salman Avestimehr"
] |
[] | 2018-06-04T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Please enter a description about the method here",
"full_name": "Lipschitz Constant Constraint",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "LCC",
"source_title": "Regularisation of Neural Networks by Enforcing Lipschitz Continuity",
"source_url": "https://arxiv.org/abs/1804.04368v3"
}
] |
https://paperswithcode.com/paper/exploiting-inherent-error-resiliency-of
|
1806.05141
| null | null |
Exploiting Inherent Error-Resiliency of Neuromorphic Computing to achieve Extreme Energy-Efficiency through Mixed-Signal Neurons
|
Neuromorphic computing, inspired by the brain, promises extreme efficiency
for certain classes of learning tasks, such as classification and pattern
recognition. The performance and power consumption of neuromorphic computing
depends heavily on the choice of the neuron architecture. Digital neurons
(Dig-N) are conventionally known to be accurate and efficient at high speed,
while suffering from high leakage currents from a large number of transistors
in a large design. On the other hand, analog/mixed-signal neurons are prone to
noise, variability and mismatch, but can lead to extremely low-power designs.
In this work, we will analyze, compare and contrast existing neuron
architectures with a proposed mixed-signal neuron (MS-N) in terms of
performance, power and noise, thereby demonstrating the applicability of the
proposed mixed-signal neuron for achieving extreme energy-efficiency in
neuromorphic computing. The proposed MS-N is implemented in 65 nm CMOS
technology and exhibits > 100X better energy-efficiency across all frequencies
over two traditional digital neurons synthesized in the same technology node.
We also demonstrate that the inherent error-resiliency of a fully connected or
even convolutional neural network (CNN) can handle the noise as well as the
manufacturing non-idealities of the MS-N up to certain degrees. Notably, a
system-level implementation on MNIST datasets exhibits a worst-case increase in
classification error by 2.1% when the integrated noise power in the bandwidth
is ~ 0.1 uV2, along with +-3{\sigma} amount of variation and mismatch
introduced in the transistor parameters for the proposed neuron with 8-bit
precision.
| null |
http://arxiv.org/abs/1806.05141v1
|
http://arxiv.org/pdf/1806.05141v1.pdf
| null |
[
"Baibhab Chatterjee",
"Priyadarshini Panda",
"Shovan Maity",
"Ayan Biswas",
"Kaushik Roy",
"Shreyas Sen"
] |
[
"General Classification"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/efficient-certifiably-optimal-clustering-with
|
1806.00530
| null | null |
Efficient, Certifiably Optimal Clustering with Applications to Latent Variable Graphical Models
|
Motivated by the task of clustering either $d$ variables or $d$ points into
$K$ groups, we investigate efficient algorithms to solve the Peng-Wei (P-W)
$K$-means semi-definite programming (SDP) relaxation. The P-W SDP has been
shown in the literature to have good statistical properties in a variety of
settings, but remains intractable to solve in practice. To this end we propose
FORCE, a new algorithm to solve this SDP relaxation. Compared to the naive
interior point method, our method reduces the computational complexity of
solving the SDP from $\tilde{O}(d^7\log\epsilon^{-1})$ to
$\tilde{O}(d^{6}K^{-2}\epsilon^{-1})$ arithmetic operations for an
$\epsilon$-optimal solution. Our method combines a primal first-order method
with a dual optimality certificate search, which when successful, allows for
early termination of the primal method. We show for certain variable clustering
problems that, with high probability, FORCE is guaranteed to find the optimal
solution to the SDP relaxation and provide a certificate of exact optimality.
As verified by our numerical experiments, this allows FORCE to solve the P-W
SDP with dimensions in the hundreds in only tens of seconds. For a variation of
the P-W SDP where $K$ is not known a priori a slight modification of FORCE
reduces the computational complexity of solving this problem as well: from
$\tilde{O}(d^7\log\epsilon^{-1})$ using a standard SDP solver to
$\tilde{O}(d^{4}\epsilon^{-1})$.
|
Compared to the naive interior point method, our method reduces the computational complexity of solving the SDP from $\tilde{O}(d^7\log\epsilon^{-1})$ to $\tilde{O}(d^{6}K^{-2}\epsilon^{-1})$ arithmetic operations for an $\epsilon$-optimal solution.
|
http://arxiv.org/abs/1806.00530v3
|
http://arxiv.org/pdf/1806.00530v3.pdf
| null |
[
"Carson Eisenach",
"Han Liu"
] |
[
"Clustering"
] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/high-dimensional-inference-for-cluster-based
|
1806.05139
| null | null |
High-Dimensional Inference for Cluster-Based Graphical Models
|
Motivated by modern applications in which one constructs graphical models based on a very large number of features, this paper introduces a new class of cluster-based graphical models, in which variable clustering is applied as an initial step for reducing the dimension of the feature space. We employ model assisted clustering, in which the clusters contain features that are similar to the same unobserved latent variable. Two different cluster-based Gaussian graphical models are considered: the latent variable graph, corresponding to the graphical model associated with the unobserved latent variables, and the cluster-average graph, corresponding to the vector of features averaged over clusters. Our study reveals that likelihood based inference for the latent graph, not analyzed previously, is analytically intractable. Our main contribution is the development and analysis of alternative estimation and inference strategies, for the precision matrix of an unobservable latent vector $Z$. We replace the likelihood of the data by an appropriate class of empirical risk functions, that can be specialized to the latent graphical model and to the simpler, but under-analyzed, cluster-average graphical model. The estimators thus derived can be used for inference on the graph structure, for instance on edge strength or pattern recovery. Inference is based on the asymptotic limits of the entry-wise estimates of the precision matrices associated with the conditional independence graphs under consideration. While taking the uncertainty induced by the clustering step into account, we establish Berry-Esseen central limit theorems for the proposed estimators. It is noteworthy that, although the clusters are estimated adaptively from the data, the central limit theorems regarding the entries of the estimated graphs are proved under the same conditions one would use if the clusters were known....
| null |
https://arxiv.org/abs/1806.05139v2
|
https://arxiv.org/pdf/1806.05139v2.pdf
| null |
[
"Carson Eisenach",
"Florentina Bunea",
"Yang Ning",
"Claudiu Dinicu"
] |
[
"Clustering",
"Vocal Bursts Intensity Prediction"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/fully-convolutional-network-for-automatic
|
1806.05182
| null | null |
Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery
|
Analysis of high-resolution satellite images has been an important research
topic for traffic management, city planning, and road monitoring. One of the
problems here is automatic and precise road extraction. From an original image,
it is difficult and computationally expensive to extract roads due to presences
of other road-like features with straight edges. In this paper, we propose an
approach for automatic road extraction based on a fully convolutional neural
network of U-net family. This network consists of ResNet-34 pre-trained on
ImageNet and decoder adapted from vanilla U-Net. Based on validation results,
leaderboard and our own experience this network shows superior results for the
DEEPGLOBE - CVPR 2018 road extraction sub-challenge. Moreover, this network
uses moderate memory that allows using just one GTX 1080 or 1080ti video cards
to perform whole training and makes pretty fast predictions.
| null |
http://arxiv.org/abs/1806.05182v2
|
http://arxiv.org/pdf/1806.05182v2.pdf
| null |
[
"Alexander V. Buslaev",
"Selim S. Seferbekov",
"Vladimir I. Iglovikov",
"Alexey A. Shvets"
] |
[
"Decoder",
"Management"
] | 2018-06-13T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/densenet.py#L113",
"description": "A **Concatenated Skip Connection** is a type of skip connection that seeks to reuse features by concatenating them to new layers, allowing more information to be retained from previous layers of the network. This contrasts with say, residual connections, where element-wise summation is used instead to incorporate information from previous layers. This type of skip connection is prominently used in DenseNets (and also Inception networks), which the Figure to the right illustrates.",
"full_name": "Concatenated Skip Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Concatenated Skip Connection",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Max Pooling** is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs.\r\n\r\nImage Source: [here](https://computersciencewiki.org/index.php/File:MaxpoolSample2.png)",
"full_name": "Max Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Max Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/milesial/Pytorch-UNet/blob/67bf11b4db4c5f2891bd7e8e7f58bcde8ee2d2db/unet/unet_model.py#L8",
"description": "**U-Net** is an architecture for semantic segmentation. It consists of a contracting path and an expansive path. The contracting path follows the typical architecture of a convolutional network. It consists of the repeated application of two 3x3 convolutions (unpadded convolutions), each followed by a rectified linear unit ([ReLU](https://paperswithcode.com/method/relu)) and a 2x2 [max pooling](https://paperswithcode.com/method/max-pooling) operation with stride 2 for downsampling. At each downsampling step we double the number of feature channels. Every step in the expansive path consists of an upsampling of the feature map followed by a 2x2 [convolution](https://paperswithcode.com/method/convolution) (“up-convolution”) that halves the number of feature channels, a concatenation with the correspondingly cropped feature map from the contracting path, and two 3x3 convolutions, each followed by a ReLU. The cropping is necessary due to the loss of border pixels in every convolution. At the final layer a [1x1 convolution](https://paperswithcode.com/method/1x1-convolution) is used to map each 64-component feature vector to the desired number of classes. In total the network has 23 convolutional layers.\r\n\r\n[Original MATLAB Code](https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/u-net-release-2015-10-02.tar.gz)",
"full_name": "U-Net",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Semantic Segmentation Models** are a class of methods that address the task of semantically segmenting an image into different object classes. Below you can find a continuously updating list of semantic segmentation models. ",
"name": "Semantic Segmentation Models",
"parent": null
},
"name": "U-Net",
"source_title": "U-Net: Convolutional Networks for Biomedical Image Segmentation",
"source_url": "http://arxiv.org/abs/1505.04597v1"
}
] |
https://paperswithcode.com/paper/generative-neural-machine-translation
|
1806.05138
| null | null |
Generative Neural Machine Translation
|
We introduce Generative Neural Machine Translation (GNMT), a latent variable
architecture which is designed to model the semantics of the source and target
sentences. We modify an encoder-decoder translation model by adding a latent
variable as a language agnostic representation which is encouraged to learn the
meaning of the sentence. GNMT achieves competitive BLEU scores on pure
translation tasks, and is superior when there are missing words in the source
sentence. We augment the model to facilitate multilingual translation and
semi-supervised learning without adding parameters. This framework
significantly reduces overfitting when there is limited paired data available,
and is effective for translating between pairs of languages not seen during
training.
| null |
http://arxiv.org/abs/1806.05138v1
|
http://arxiv.org/pdf/1806.05138v1.pdf
|
NeurIPS 2018 12
|
[
"Harshil Shah",
"David Barber"
] |
[
"Decoder",
"Machine Translation",
"Sentence",
"Translation"
] | 2018-06-13T00:00:00 |
http://papers.nips.cc/paper/7409-generative-neural-machine-translation
|
http://papers.nips.cc/paper/7409-generative-neural-machine-translation.pdf
|
generative-neural-machine-translation-1
| null |
[] |
https://paperswithcode.com/paper/marginal-policy-gradients-a-unified-family-of
|
1806.05134
| null |
HkgqFiAcFm
|
Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications
|
Many complex domains, such as robotics control and real-time strategy (RTS)
games, require an agent to learn a continuous control. In the former, an agent
learns a policy over $\mathbb{R}^d$ and in the latter, over a discrete set of
actions each of which is parametrized by a continuous parameter. Such problems
are naturally solved using policy based reinforcement learning (RL) methods,
but unfortunately these often suffer from high variance leading to instability
and slow convergence. Unnecessary variance is introduced whenever policies over
bounded action spaces are modeled using distributions with unbounded support by
applying a transformation $T$ to the sampled action before execution in the
environment. Recently, the variance reduced clipped action policy gradient
(CAPG) was introduced for actions in bounded intervals, but to date no variance
reduced methods exist when the action is a direction, something often seen in
RTS games. To this end we introduce the angular policy gradient (APG), a
stochastic policy gradient method for directional control. With the marginal
policy gradients family of estimators we present a unified analysis of the
variance reduction properties of APG and CAPG; our results provide a stronger
guarantee than existing analyses for CAPG. Experimental results on a popular
RTS game and a navigation task show that the APG estimator offers a substantial
improvement over the standard policy gradient.
|
In the former, an agent learns a policy over $\mathbb{R}^d$ and in the latter, over a discrete set of actions each of which is parametrized by a continuous parameter.
|
http://arxiv.org/abs/1806.05134v3
|
http://arxiv.org/pdf/1806.05134v3.pdf
|
ICLR 2019 5
|
[
"Carson Eisenach",
"Haichuan Yang",
"Ji Liu",
"Han Liu"
] |
[
"continuous-control",
"Continuous Control",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-13T00:00:00 |
https://openreview.net/forum?id=HkgqFiAcFm
|
https://openreview.net/pdf?id=HkgqFiAcFm
|
marginal-policy-gradients-a-unified-family-of-1
| null |
[] |
https://paperswithcode.com/paper/quantifying-the-dynamics-of-topical
|
1806.00699
| null | null |
Quantifying the dynamics of topical fluctuations in language
|
The availability of large diachronic corpora has provided the impetus for a growing body of quantitative research on language evolution and meaning change. The central quantities in this research are token frequencies of linguistic elements in texts, with changes in frequency taken to reflect the popularity or selective fitness of an element. However, corpus frequencies may change for a wide variety of reasons, including purely random sampling effects, or because corpora are composed of contemporary media and fiction texts within which the underlying topics ebb and flow with cultural and socio-political trends. In this work, we introduce a simple model for controlling for topical fluctuations in corpora - the topical-cultural advection model - and demonstrate how it provides a robust baseline of variability in word frequency changes over time. We validate the model on a diachronic corpus spanning two centuries, and a carefully-controlled artificial language change scenario, and then use it to correct for topical fluctuations in historical time series. Finally, we use the model to show that the emergence of new words typically corresponds with the rise of a trending topic. This suggests that some lexical innovations occur due to growing communicative need in a subspace of the lexicon, and that the topical-cultural advection model can be used to quantify this.
|
In this work, we introduce a simple model for controlling for topical fluctuations in corpora - the topical-cultural advection model - and demonstrate how it provides a robust baseline of variability in word frequency changes over time.
|
https://arxiv.org/abs/1806.00699v3
|
https://arxiv.org/pdf/1806.00699v3.pdf
| null |
[
"Andres Karjus",
"Richard A. Blythe",
"Simon Kirby",
"Kenny Smith"
] |
[
"Time Series",
"Time Series Analysis"
] | 2018-06-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/what-is-it-like-down-there-generating-dense
|
1806.05129
| null | null |
What Is It Like Down There? Generating Dense Ground-Level Views and Image Features From Overhead Imagery Using Conditional Generative Adversarial Networks
|
This paper investigates conditional generative adversarial networks (cGANs)
to overcome a fundamental limitation of using geotagged media for geographic
discovery, namely its sparse and uneven spatial distribution. We train a cGAN
to generate ground-level views of a location given overhead imagery. We show
the "fake" ground-level images are natural looking and are structurally similar
to the real images. More significantly, we show the generated images are
representative of the locations and that the representations learned by the
cGANs are informative. In particular, we show that dense feature maps generated
using our framework are more effective for land-cover classification than
approaches which spatially interpolate features extracted from sparse
ground-level images. To our knowledge, ours is the first work to use cGANs to
generate ground-level views given overhead imagery and to explore the benefits
of the learned representations.
| null |
http://arxiv.org/abs/1806.05129v2
|
http://arxiv.org/pdf/1806.05129v2.pdf
| null |
[
"Xueqing Deng",
"Yi Zhu",
"Shawn Newsam"
] |
[
"General Classification",
"Land Cover Classification"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/online-multi-object-tracking-with-historical
|
1805.10916
| null | null |
Online Multi-Object Tracking with Historical Appearance Matching and Scene Adaptive Detection Filtering
|
In this paper, we propose the methods to handle temporal errors during
multi-object tracking. Temporal error occurs when objects are occluded or noisy
detections appear near the object. In those situations, tracking may fail and
various errors like drift or ID-switching occur. It is hard to overcome
temporal errors only by using motion and shape information. So, we propose the
historical appearance matching method and joint-input siamese network which was
trained by 2-step process. It can prevent tracking failures although objects
are temporally occluded or last matching information is unreliable. We also
provide useful technique to remove noisy detections effectively according to
scene condition. Tracking performance, especially identity consistency, is
highly improved by attaching our methods.
| null |
http://arxiv.org/abs/1805.10916v4
|
http://arxiv.org/pdf/1805.10916v4.pdf
| null |
[
"Young-chul Yoon",
"Abhijeet Boragule",
"Young-min Song",
"Kwangjin Yoon",
"Moongu Jeon"
] |
[
"Multi-Object Tracking",
"Object",
"Object Tracking",
"Online Multi-Object Tracking"
] | 2018-05-28T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "A **Siamese Network** consists of twin networks which accept distinct inputs but are joined by an energy function at the top. This function computes a metric between the highest level feature representation on each side. The parameters between the twin networks are tied. [Weight tying](https://paperswithcode.com/method/weight-tying) guarantees that two extremely similar images are not mapped by each network to very different locations in feature space because each network computes the same function. The network is symmetric, so that whenever we present two distinct images to the twin networks, the top conjoining layer will compute the same metric as if we were to we present the same two images but to the opposite twins.\r\n\r\nIntuitively instead of trying to classify inputs, a siamese network learns to differentiate between inputs, learning their similarity. The loss function used is usually a form of contrastive loss.\r\n\r\nSource: [Koch et al](https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf)",
"full_name": "Siamese Network",
"introduced_year": 1993,
"main_collection": {
"area": "General",
"description": "**Twin Networks** are a type of neural network architecture where we use two of the same network architecture to perform a task. For example, Siamese Networks are used to learn representations that differentiate between inputs (learning their similarity). Below you can find a continuously updating list of twin network architectures.",
"name": "Twin Networks",
"parent": null
},
"name": "Siamese Network",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/learning-to-shoot-in-first-person-shooter
|
1806.05117
| null | null |
Learning to Shoot in First Person Shooter Games by Stabilizing Actions and Clustering Rewards for Reinforcement Learning
|
While reinforcement learning (RL) has been applied to turn-based board games
for many years, more complex games involving decision-making in real-time are
beginning to receive more attention. A challenge in such environments is that
the time that elapses between deciding to take an action and receiving a reward
based on its outcome can be longer than the interval between successive
decisions. We explore this in the context of a non-player character (NPC) in a
modern first-person shooter game. Such games take place in 3D environments
where players, both human and computer-controlled, compete by engaging in
combat and completing task objectives. We investigate the use of RL to enable
NPCs to gather experience from game-play and improve their shooting skill over
time from a reward signal based on the damage caused to opponents. We propose a
new method for RL updates and reward calculations, in which the updates are
carried out periodically, after each shooting encounter has ended, and a new
weighted-reward mechanism is used which increases the reward applied to actions
that lead to damaging the opponent in successive hits in what we term "hit
clusters".
|
While reinforcement learning (RL) has been applied to turn-based board games for many years, more complex games involving decision-making in real-time are beginning to receive more attention.
|
http://arxiv.org/abs/1806.05117v1
|
http://arxiv.org/pdf/1806.05117v1.pdf
| null |
[
"Frank G. Glavin",
"Michael G. Madden"
] |
[
"Board Games",
"Clustering",
"Decision Making",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-retrospective-analysis-of-the-fake-news-1
|
1806.05180
| null | null |
A Retrospective Analysis of the Fake News Challenge Stance Detection Task
|
The 2017 Fake News Challenge Stage 1 (FNC-1) shared task addressed a stance
classification task as a crucial first step towards detecting fake news. To
date, there is no in-depth analysis paper to critically discuss FNC-1's
experimental setup, reproduce the results, and draw conclusions for
next-generation stance classification methods. In this paper, we provide such
an in-depth analysis for the three top-performing systems. We first find that
FNC-1's proposed evaluation metric favors the majority class, which can be
easily classified, and thus overestimates the true discriminative power of the
methods. Therefore, we propose a new F1-based metric yielding a changed system
ranking. Next, we compare the features and architectures used, which leads to a
novel feature-rich stacked LSTM model that performs on par with the best
systems, but is superior in predicting minority classes. To understand the
methods' ability to generalize, we derive a new dataset and perform both
in-domain and cross-domain experiments. Our qualitative and quantitative study
helps interpreting the original FNC-1 scores and understand which features help
improving performance and why. Our new dataset and all source code used during
the reproduction study are publicly available for future research.
|
To date, there is no in-depth analysis paper to critically discuss FNC-1's experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods.
|
http://arxiv.org/abs/1806.05180v1
|
http://arxiv.org/pdf/1806.05180v1.pdf
| null |
[
"Andreas Hanselowski",
"Avinesh PVS",
"Benjamin Schiller",
"Felix Caspelherr",
"Debanjan Chaudhuri",
"Christian M. Meyer",
"Iryna Gurevych"
] |
[
"General Classification",
"Stance Classification",
"Stance Detection"
] | 2018-06-13T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\\right)}$$\r\n\r\nSome drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.",
"full_name": "Sigmoid Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Sigmoid Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nHistorically, the tanh function became preferred over the [sigmoid function](https://paperswithcode.com/method/sigmoid-activation) as it gave better performance for multi-layer neural networks. But it did not solve the vanishing gradient problem that sigmoids suffered, which was tackled more effectively with the introduction of [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nImage Source: [Junxi Feng](https://www.researchgate.net/profile/Junxi_Feng)",
"full_name": "Tanh Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Tanh Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.\r\n\r\n(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))\r\n\r\n(Introduced by Hochreiter and Schmidhuber)",
"full_name": "Long Short-Term Memory",
"introduced_year": 1997,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "LSTM",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/a-physical-model-for-efficient-ranking-in
|
1709.09002
| null | null |
A physical model for efficient ranking in networks
|
We present a physically-inspired model and an efficient algorithm to infer
hierarchical rankings of nodes in directed networks. It assigns real-valued
ranks to nodes rather than simply ordinal ranks, and it formalizes the
assumption that interactions are more likely to occur between individuals with
similar ranks. It provides a natural statistical significance test for the
inferred hierarchy, and it can be used to perform inference tasks such as
predicting the existence or direction of edges. The ranking is obtained by
solving a linear system of equations, which is sparse if the network is; thus
the resulting algorithm is extremely efficient and scalable. We illustrate
these findings by analyzing real and synthetic data, including datasets from
animal behavior, faculty hiring, social support networks, and sports
tournaments. We show that our method often outperforms a variety of others, in
both speed and accuracy, in recovering the underlying ranks and predicting edge
directions.
|
We present a physically-inspired model and an efficient algorithm to infer hierarchical rankings of nodes in directed networks.
|
http://arxiv.org/abs/1709.09002v4
|
http://arxiv.org/pdf/1709.09002v4.pdf
| null |
[
"Caterina De Bacco",
"Daniel B. Larremore",
"Cristopher Moore"
] |
[
"model"
] | 2017-09-03T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/comparing-fairness-criteria-based-on-social
|
1806.05112
| null | null |
Comparing Fairness Criteria Based on Social Outcome
|
Fairness in algorithmic decision-making processes is attracting increasing
concern. When an algorithm is applied to human-related decision-making an
estimator solely optimizing its predictive power can learn biases on the
existing data, which motivates us the notion of fairness in machine learning.
while several different notions are studied in the literature, little studies
are done on how these notions affect the individuals. We demonstrate such a
comparison between several policies induced by well-known fairness criteria,
including the color-blind (CB), the demographic parity (DP), and the equalized
odds (EO). We show that the EO is the only criterion among them that removes
group-level disparity. Empirical studies on the social welfare and disparity of
these policies are conducted.
| null |
http://arxiv.org/abs/1806.05112v1
|
http://arxiv.org/pdf/1806.05112v1.pdf
| null |
[
"Junpei Komiyama",
"Hajime Shimao"
] |
[
"Decision Making",
"Fairness"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/knowledge-amalgam-generating-jokes-and-quotes
|
1806.04387
| null | null |
Knowledge Amalgam: Generating Jokes and Quotes Together
|
Generating humor and quotes are very challenging problems in the field of
computational linguistics and are often tackled separately. In this paper, we
present a controlled Long Short-Term Memory (LSTM) architecture which is
trained with categorical data like jokes and quotes together by passing
category as an input along with the sequence of words. The idea is that a
single neural net will learn the structure of both jokes and quotes to generate
them on demand according to input category. Importantly, we believe the neural
net has more knowledge as it's trained on different datasets and hence will
enable it to generate more creative jokes or quotes from the mixture of
information. May the network generate a funny inspirational joke!
| null |
http://arxiv.org/abs/1806.04387v2
|
http://arxiv.org/pdf/1806.04387v2.pdf
| null |
[
"Bhargav Chippada",
"Shubajit Saha"
] |
[] | 2018-06-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/dre-bot-a-hierarchical-first-person-shooter
|
1806.05106
| null | null |
DRE-Bot: A Hierarchical First Person Shooter Bot Using Multiple Sarsa(λ) Reinforcement Learners
|
This paper describes an architecture for controlling non-player characters
(NPC) in the First Person Shooter (FPS) game Unreal Tournament 2004.
Specifically, the DRE-Bot architecture is made up of three reinforcement
learners, Danger, Replenish and Explore, which use the tabular Sarsa({\lambda})
algorithm. This algorithm enables the NPC to learn through trial and error
building up experience over time in an approach inspired by human learning.
Experimentation is carried to measure the performance of DRE-Bot when competing
against fixed strategy bots that ship with the game. The discount parameter,
{\gamma}, and the trace parameter, {\lambda}, are also varied to see if their
values have an effect on the performance.
| null |
http://arxiv.org/abs/1806.05106v1
|
http://arxiv.org/pdf/1806.05106v1.pdf
| null |
[
"Frank G. Glavin",
"Michael G. Madden"
] |
[] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/improving-cytoarchitectonic-segmentation-of
|
1806.05104
| null | null |
Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks
|
Cytoarchitectonic parcellations of the human brain serve as anatomical
references in multimodal atlas frameworks. They are based on analysis of
cell-body stained histological sections and the identification of borders
between brain areas. The de-facto standard involves a semi-automatic,
reproducible border detection, but does not scale with high-throughput imaging
in large series of sections at microscopical resolution. Automatic
parcellation, however, is extremely challenging due to high variation in the
data, and the need for a large field of view at microscopic resolution. The
performance of a recently proposed Convolutional Neural Network model that
addresses this problem especially suffers from the naturally limited amount of
expert annotations for training. To circumvent this limitation, we propose to
pre-train neural networks on a self-supervised auxiliary task, predicting the
3D distance between two patches sampled from the same brain. Compared to a
random initialization, fine-tuning from these networks results in significantly
better segmentations. We show that the self-supervised model has implicitly
learned to distinguish several cortical brain areas -- a strong indicator that
the proposed auxiliary task is appropriate for cytoarchitectonic mapping.
| null |
http://arxiv.org/abs/1806.05104v1
|
http://arxiv.org/pdf/1806.05104v1.pdf
| null |
[
"Hannah Spitzer",
"Kai Kiwitz",
"Katrin Amunts",
"Stefan Harmeling",
"Timo Dickscheid"
] |
[] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/graph-based-decoding-for-event-sequencing-and-1
|
1806.05099
| null | null |
Graph-Based Decoding for Event Sequencing and Coreference Resolution
|
Events in text documents are interrelated in complex ways. In this paper, we
study two types of relation: Event Coreference and Event Sequencing. We show
that the popular tree-like decoding structure for automated Event Coreference
is not suitable for Event Sequencing. To this end, we propose a graph-based
decoding algorithm that is applicable to both tasks. The new decoding algorithm
supports flexible feature sets for both tasks. Empirically, our event
coreference system has achieved state-of-the-art performance on the TAC-KBP
2015 event coreference task and our event sequencing system beats a strong
temporal-based, oracle-informed baseline. We discuss the challenges of studying
these event relations.
| null |
http://arxiv.org/abs/1806.05099v1
|
http://arxiv.org/pdf/1806.05099v1.pdf
| null |
[
"Zhengzhong Liu",
"Teruko Mitamura",
"Eduard Hovy"
] |
[
"coreference-resolution",
"Coreference Resolution"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/spectral-network-embedding-a-fast-and
|
1806.02623
| null | null |
Spectral Network Embedding: A Fast and Scalable Method via Sparsity
|
Network embedding aims to learn low-dimensional representations of nodes in a
network, while the network structure and inherent properties are preserved. It
has attracted tremendous attention recently due to significant progress in
downstream network learning tasks, such as node classification, link
prediction, and visualization. However, most existing network embedding methods
suffer from the expensive computations due to the large volume of networks. In
this paper, we propose a $10\times \sim 100\times$ faster network embedding
method, called Progle, by elegantly utilizing the sparsity property of online
networks and spectral analysis. In Progle, we first construct a \textit{sparse}
proximity matrix and train the network embedding efficiently via sparse matrix
decomposition. Then we introduce a network propagation pattern via spectral
analysis to incorporate local and global structure information into the
embedding. Besides, this model can be generalized to integrate network
information into other insufficiently trained embeddings at speed. Benefiting
from sparse spectral network embedding, our experiment on four different
datasets shows that Progle outperforms or is comparable to state-of-the-art
unsupervised comparison approaches---DeepWalk, LINE, node2vec, GraRep, and
HOPE, regarding accuracy, while is $10\times$ faster than the fastest
word2vec-based method. Finally, we validate the scalability of Progle both in
real large-scale networks and multiple scales of synthetic networks.
|
In this paper, we propose a $10\times \sim 100\times$ faster network embedding method, called Progle, by elegantly utilizing the sparsity property of online networks and spectral analysis.
|
http://arxiv.org/abs/1806.02623v2
|
http://arxiv.org/pdf/1806.02623v2.pdf
| null |
[
"Jie Zhang",
"Yan Wang",
"Jie Tang",
"Ming Ding"
] |
[
"Link Prediction",
"Network Embedding",
"Node Classification"
] | 2018-06-07T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Graph Representation with Global structure",
"introduced_year": 2000,
"main_collection": {
"area": "Graphs",
"description": "\n\ngraph embeddings, can be homogeneous graph or heterogeneous graph",
"name": "Graph Embeddings",
"parent": null
},
"name": "GraRep",
"source_title": "GraRep: Learning Graph Representations with Global Structural Information",
"source_url": "https://www.researchgate.net/publication/301417811_GraRep"
},
{
"code_snippet_url": null,
"description": "**node2vec** is a framework for learning graph embeddings for nodes in graphs. Node2vec maximizes a likelihood objective over mappings which preserve neighbourhood distances in higher dimensional spaces. From an algorithm design perspective, node2vec exploits the freedom to define neighbourhoods for nodes and provide an explanation for the effect of the choice of neighborhood on the learned representations. \r\n\r\nFor each node, node2vec simulates biased random walks based on an efficient network-aware search strategy and the nodes appearing in the random walk define neighbourhoods. The search strategy accounts for the relative influence nodes exert in a network. It also generalizes prior work alluding to naive search strategies by providing flexibility in exploring neighborhoods.",
"full_name": "node2vec",
"introduced_year": 2000,
"main_collection": {
"area": "Graphs",
"description": "\n\ngraph embeddings, can be homogeneous graph or heterogeneous graph",
"name": "Graph Embeddings",
"parent": null
},
"name": "node2vec",
"source_title": "node2vec: Scalable Feature Learning for Networks",
"source_url": "http://arxiv.org/abs/1607.00653v1"
}
] |
https://paperswithcode.com/paper/introducing-user-prescribed-constraints-in
|
1806.05096
| null | null |
Introducing user-prescribed constraints in Markov chains for nonlinear dimensionality reduction
|
Stochastic kernel based dimensionality reduction approaches have become
popular in the last decade. The central component of many of these methods is a
symmetric kernel that quantifies the vicinity between pairs of data points and
a kernel-induced Markov chain on the data. Typically, the Markov chain is fully
specified by the kernel through row normalization. However, in many cases, it
is desirable to impose user-specified stationary-state and dynamical
constraints on the Markov chain. Unfortunately, no systematic framework exists
to impose such user-defined constraints. Here, we introduce a path entropy
maximization based approach to derive the transition probabilities of Markov
chains using a kernel and additional user-specified constraints. We illustrate
the usefulness of these Markov chains with examples.
|
The central component of many of these methods is a symmetric kernel that quantifies the vicinity between pairs of data points and a kernel-induced Markov chain on the data.
|
http://arxiv.org/abs/1806.05096v2
|
http://arxiv.org/pdf/1806.05096v2.pdf
| null |
[
"Purushottam D. Dixit"
] |
[
"Dimensionality Reduction"
] | 2018-06-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/tempered-adversarial-networks
|
1802.04374
| null | null |
Tempered Adversarial Networks
|
Generative adversarial networks (GANs) have been shown to produce realistic
samples from high-dimensional distributions, but training them is considered
hard. A possible explanation for training instabilities is the inherent
imbalance between the networks: While the discriminator is trained directly on
both real and fake samples, the generator only has control over the fake
samples it produces since the real data distribution is fixed by the choice of
a given dataset. We propose a simple modification that gives the generator
control over the real samples which leads to a tempered learning process for
both generator and discriminator. The real data distribution passes through a
lens before being revealed to the discriminator, balancing the generator and
discriminator by gradually revealing more detailed features necessary to
produce high-quality results. The proposed module automatically adjusts the
learning process to the current strength of the networks, yet is generic and
easy to add to any GAN variant. In a number of experiments, we show that this
can improve quality, stability and/or convergence speed across a range of
different GAN architectures (DCGAN, LSGAN, WGAN-GP).
| null |
http://arxiv.org/abs/1802.04374v4
|
http://arxiv.org/pdf/1802.04374v4.pdf
|
ICML 2018 7
|
[
"Mehdi S. M. Sajjadi",
"Giambattista Parascandolo",
"Arash Mehrjou",
"Bernhard Schölkopf"
] |
[] | 2018-02-12T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=1870
|
http://proceedings.mlr.press/v80/sajjadi18a/sajjadi18a.pdf
|
tempered-adversarial-networks-1
| null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google/jax/blob/36f91261099b00194922bd93ed1286fe1c199724/jax/experimental/stax.py#L116",
"description": "**Batch Normalization** aims to reduce internal covariate shift, and in doing so aims to accelerate the training of deep neural nets. It accomplishes this via a normalization step that fixes the means and variances of layer inputs. Batch Normalization also has a beneficial effect on the gradient flow through the network, by reducing the dependence of gradients on the scale of the parameters or of their initial values. This allows for use of much higher learning rates without the risk of divergence. Furthermore, batch normalization regularizes the model and reduces the need for [Dropout](https://paperswithcode.com/method/dropout).\r\n\r\nWe apply a batch normalization layer as follows for a minibatch $\\mathcal{B}$:\r\n\r\n$$ \\mu\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}x\\_{i} $$\r\n\r\n$$ \\sigma^{2}\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}\\left(x\\_{i}-\\mu\\_{\\mathcal{B}}\\right)^{2} $$\r\n\r\n$$ \\hat{x}\\_{i} = \\frac{x\\_{i} - \\mu\\_{\\mathcal{B}}}{\\sqrt{\\sigma^{2}\\_{\\mathcal{B}}+\\epsilon}} $$\r\n\r\n$$ y\\_{i} = \\gamma\\hat{x}\\_{i} + \\beta = \\text{BN}\\_{\\gamma, \\beta}\\left(x\\_{i}\\right) $$\r\n\r\nWhere $\\gamma$ and $\\beta$ are learnable parameters.",
"full_name": "Batch Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Batch Normalization",
"source_title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
"source_url": "http://arxiv.org/abs/1502.03167v3"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "How do I get a human at Expedia?\r\nHow Do I Get a Human at Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Real-Time Help & Exclusive Travel Deals!Want to speak with a real person at Expedia? Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now for immediate support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Skip the wait, get fast answers, and enjoy limited-time offers that make your next journey more affordable and stress-free. Call today and save!\r\n\r\nHow do I get a human at Expedia?\r\nHow Do I Get a Human at Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Real-Time Help & Exclusive Travel Deals!Want to speak with a real person at Expedia? Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now for immediate support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Skip the wait, get fast answers, and enjoy limited-time offers that make your next journey more affordable and stress-free. Call today and save!",
"full_name": "HuMan(Expedia)||How do I get a human at Expedia?",
"introduced_year": 2014,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "HuMan(Expedia)||How do I get a human at Expedia?",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/eriklindernoren/PyTorch-GAN/blob/a163b82beff3d01688d8315a3fd39080400e7c01/implementations/lsgan/lsgan.py#L102",
"description": "**GAN Least Squares Loss** is a least squares loss function for generative adversarial networks. Minimizing this objective function is equivalent to minimizing the Pearson $\\chi^{2}$ divergence. The objective function (here for [LSGAN](https://paperswithcode.com/method/lsgan)) can be defined as:\r\n\r\n$$ \\min\\_{D}V\\_{LS}\\left(D\\right) = \\frac{1}{2}\\mathbb{E}\\_{\\mathbf{x} \\sim p\\_{data}\\left(\\mathbf{x}\\right)}\\left[\\left(D\\left(\\mathbf{x}\\right) - b\\right)^{2}\\right] + \\frac{1}{2}\\mathbb{E}\\_{\\mathbf{z}\\sim p\\_{data}\\left(\\mathbf{z}\\right)}\\left[\\left(D\\left(G\\left(\\mathbf{z}\\right)\\right) - a\\right)^{2}\\right] $$\r\n\r\n$$ \\min\\_{G}V\\_{LS}\\left(G\\right) = \\frac{1}{2}\\mathbb{E}\\_{\\mathbf{z} \\sim p\\_{\\mathbf{z}}\\left(\\mathbf{z}\\right)}\\left[\\left(D\\left(G\\left(\\mathbf{z}\\right)\\right) - c\\right)^{2}\\right] $$\r\n\r\nwhere $a$ and $b$ are the labels for fake data and real data and $c$ denotes the value that $G$ wants $D$ to believe for fake data.",
"full_name": "GAN Least Squares Loss",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Loss Functions** are used to frame the problem to be optimized within deep learning. Below you will find a continuously updating list of (specialized) loss functions for neutral networks.",
"name": "Loss Functions",
"parent": null
},
"name": "GAN Least Squares Loss",
"source_title": "Least Squares Generative Adversarial Networks",
"source_url": "http://arxiv.org/abs/1611.04076v3"
},
{
"code_snippet_url": "https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/lsgan/lsgan.py",
"description": "**LSGAN**, or **Least Squares GAN**, is a type of generative adversarial network that adopts the least squares loss function for the discriminator. Minimizing the objective function of LSGAN yields minimizing the Pearson $\\chi^{2}$ divergence. The objective function can be defined as:\r\n\r\n$$ \\min\\_{D}V\\_{LSGAN}\\left(D\\right) = \\frac{1}{2}\\mathbb{E}\\_{\\mathbf{x} \\sim p\\_{data}\\left(\\mathbf{x}\\right)}\\left[\\left(D\\left(\\mathbf{x}\\right) - b\\right)^{2}\\right] + \\frac{1}{2}\\mathbb{E}\\_{\\mathbf{z}\\sim p\\_{\\mathbf{z}}\\left(\\mathbf{z}\\right)}\\left[\\left(D\\left(G\\left(\\mathbf{z}\\right)\\right) - a\\right)^{2}\\right] $$\r\n\r\n$$ \\min\\_{G}V\\_{LSGAN}\\left(G\\right) = \\frac{1}{2}\\mathbb{E}\\_{\\mathbf{z} \\sim p\\_{\\mathbf{z}}\\left(\\mathbf{z}\\right)}\\left[\\left(D\\left(G\\left(\\mathbf{z}\\right)\\right) - c\\right)^{2}\\right] $$\r\n\r\nwhere $a$ and $b$ are the labels for fake data and real data and $c$ denotes the value that $G$ wants $D$ to believe for fake data.",
"full_name": "LSGAN",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "LSGAN",
"source_title": "Least Squares Generative Adversarial Networks",
"source_url": "http://arxiv.org/abs/1611.04076v3"
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "In today’s digital age, Dogecoin has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're trying to recover a lost Dogecoin wallet, knowing where to get help is essential. That’s why the Dogecoin customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Dogecoin Customer Support Number +1-833-534-1729\r\nDogecoin operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Dogecoin Transaction Not Confirmed\r\nOne of the most common concerns is when a Dogecoin transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Dogecoin Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Dogecoin wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Dogecoin Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Dogecoin wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Dogecoin Deposit Not Received\r\nIf someone has sent you Dogecoin but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Dogecoin deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Dogecoin Transaction Stuck or Pending\r\nSometimes your Dogecoin transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. 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Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Dogecoin tech.\r\n\r\n24/7 Availability: Dogecoin doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Dogecoin Support and Wallet Issues\r\nQ1: Can Dogecoin support help me recover stolen BTC?\r\nA: While Dogecoin transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Dogecoin transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Dogecoin’s official number (Dogecoin is decentralized), it connects you to trained professionals experienced in resolving all major Dogecoin issues.\r\n\r\nFinal Thoughts\r\nDogecoin is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Dogecoin customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Dogecoin Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Dogecoin Customer Service Number +1-833-534-1729",
"source_title": "Generative Adversarial Networks",
"source_url": "https://arxiv.org/abs/1406.2661v1"
}
] |
https://paperswithcode.com/paper/estimating-achilles-tendon-healing-progress
|
1806.05091
| null | null |
Estimating Achilles tendon healing progress with convolutional neural networks
|
Quantitative assessment of a treatment progress in the Achilles tendon
healing process - one of the most common musculoskeletal disorder in modern
medical practice - is typically a long and complex process: multiple MRI
protocols need to be acquired and analysed by radiology experts. In this paper,
we propose to significantly reduce the complexity of this assessment using a
novel method based on a pre-trained convolutional neural network. We first
train our neural network on over 500,000 2D axial cross-sections from over 3000
3D MRI studies to classify MRI images as belonging to a healthy or injured
class, depending on the patient's condition. We then take the outputs of
modified pre-trained network and apply linear regression on the PCA-reduced
space of the features to assess treatment progress. Our method allows to reduce
up to 5-fold the amount of data needed to be registered during the MRI scan
without any information loss. Furthermore, we are able to predict the healing
process phase with equal accuracy to human experts in 3 out of 6 main criteria.
Finally, contrary to the current approaches to regeneration assessment that
rely on radiologist subjective opinion, our method allows to objectively
compare different treatments methods which can lead to improved diagnostics and
patient's recovery.
| null |
http://arxiv.org/abs/1806.05091v2
|
http://arxiv.org/pdf/1806.05091v2.pdf
| null |
[
"Norbert Kapinski",
"Jakub Zielinski",
"Bartosz A. Borucki",
"Tomasz Trzcinski",
"Beata Ciszkowska-Lyson",
"Krzysztof S. Nowinski"
] |
[] | 2018-06-13T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Linear Regression** is a method for modelling a relationship between a dependent variable and independent variables. These models can be fit with numerous approaches. The most common is *least squares*, where we minimize the mean square error between the predicted values $\\hat{y} = \\textbf{X}\\hat{\\beta}$ and actual values $y$: $\\left(y-\\textbf{X}\\beta\\right)^{2}$.\r\n\r\nWe can also define the problem in probabilistic terms as a generalized linear model (GLM) where the pdf is a Gaussian distribution, and then perform maximum likelihood estimation to estimate $\\hat{\\beta}$.\r\n\r\nImage Source: [Wikipedia](https://en.wikipedia.org/wiki/Linear_regression)",
"full_name": "Linear Regression",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Generalized Linear Models (GLMs)** are a class of models that generalize upon linear regression by allowing many more distributions to be modeled for the response variable via a link function. Below you can find a continuously updating list of GLMs.",
"name": "Generalized Linear Models",
"parent": null
},
"name": "Linear Regression",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/learning-distributions-of-shape-trajectories
|
1803.10119
| null | null |
Learning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms
|
We propose a method to learn a distribution of shape trajectories from
longitudinal data, i.e. the collection of individual objects repeatedly
observed at multiple time-points. The method allows to compute an average
spatiotemporal trajectory of shape changes at the group level, and the
individual variations of this trajectory both in terms of geometry and time
dynamics. First, we formulate a non-linear mixed-effects statistical model as
the combination of a generic statistical model for manifold-valued longitudinal
data, a deformation model defining shape trajectories via the action of a
finite-dimensional set of diffeomorphisms with a manifold structure, and an
efficient numerical scheme to compute parallel transport on this manifold.
Second, we introduce a MCMC-SAEM algorithm with a specific approach to shape
sampling, an adaptive scheme for proposal variances, and a log-likelihood
tempering strategy to estimate our model. Third, we validate our algorithm on
2D simulated data, and then estimate a scenario of alteration of the shape of
the hippocampus 3D brain structure during the course of Alzheimer's disease.
The method shows for instance that hippocampal atrophy progresses more quickly
in female subjects, and occurs earlier in APOE4 mutation carriers. We finally
illustrate the potential of our method for classifying pathological
trajectories versus normal ageing.
| null |
http://arxiv.org/abs/1803.10119v2
|
http://arxiv.org/pdf/1803.10119v2.pdf
|
CVPR 2018 6
|
[
"Alexandre Bône",
"Olivier Colliot",
"Stanley Durrleman"
] |
[
"Hippocampus"
] | 2018-03-27T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Bone_Learning_Distributions_of_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Bone_Learning_Distributions_of_CVPR_2018_paper.pdf
|
learning-distributions-of-shape-trajectories-1
| null |
[] |
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