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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. 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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. 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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
[]