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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
https://paperswithcode.com/paper/deploying-deep-ranking-models-for-search
|
1806.02281
| null | null |
Deploying Deep Ranking Models for Search Verticals
|
In this paper, we present an architecture executing a complex machine
learning model such as a neural network capturing semantic similarity between a
query and a document; and deploy to a real-world production system serving
500M+users. We present the challenges that arise in a real-world system and how
we solve them. We demonstrate that our architecture provides competitive
modeling capability without any significant performance impact to the system in
terms of latency. Our modular solution and insights can be used by other
real-world search systems to realize and productionize recent gains in neural
networks.
| null |
http://arxiv.org/abs/1806.02281v1
|
http://arxiv.org/pdf/1806.02281v1.pdf
| null |
[
"Rohan Ramanath",
"Gungor Polatkan",
"Liqin Xu",
"Harold Lee",
"Bo Hu",
"Shan Zhou"
] |
[
"BIG-bench Machine Learning",
"Semantic Similarity",
"Semantic Textual Similarity"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/fast-information-theoretic-bayesian
|
1711.00673
| null | null |
Fast Information-theoretic Bayesian Optimisation
|
Information-theoretic Bayesian optimisation techniques have demonstrated
state-of-the-art performance in tackling important global optimisation
problems. However, current information-theoretic approaches require many
approximations in implementation, introduce often-prohibitive computational
overhead and limit the choice of kernels available to model the objective. We
develop a fast information-theoretic Bayesian Optimisation method, FITBO, that
avoids the need for sampling the global minimiser, thus significantly reducing
computational overhead. Moreover, in comparison with existing approaches, our
method faces fewer constraints on kernel choice and enjoys the merits of
dealing with the output space. We demonstrate empirically that FITBO inherits
the performance associated with information-theoretic Bayesian optimisation,
while being even faster than simpler Bayesian optimisation approaches, such as
Expected Improvement.
|
Information-theoretic Bayesian optimisation techniques have demonstrated state-of-the-art performance in tackling important global optimisation problems.
|
http://arxiv.org/abs/1711.00673v5
|
http://arxiv.org/pdf/1711.00673v5.pdf
|
ICML 2018 7
|
[
"Binxin Ru",
"Mark McLeod",
"Diego Granziol",
"Michael A. Osborne"
] |
[
"Bayesian Optimisation"
] | 2017-11-02T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=1973
|
http://proceedings.mlr.press/v80/ru18a/ru18a.pdf
|
fast-information-theoretic-bayesian-1
| null |
[] |
https://paperswithcode.com/paper/deep-vessel-segmentation-by-learning
|
1806.02279
| null | null |
Deep Vessel Segmentation By Learning Graphical Connectivity
|
We propose a novel deep-learning-based system for vessel segmentation.
Existing methods using CNNs have mostly relied on local appearances learned on
the regular image grid, without considering the graphical structure of vessel
shape. To address this, we incorporate a graph convolutional network into a
unified CNN architecture, where the final segmentation is inferred by combining
the different types of features. The proposed method can be applied to expand
any type of CNN-based vessel segmentation method to enhance the performance.
Experiments show that the proposed method outperforms the current
state-of-the-art methods on two retinal image datasets as well as a coronary
artery X-ray angiography dataset.
|
We propose a novel deep-learning-based system for vessel segmentation.
|
http://arxiv.org/abs/1806.02279v1
|
http://arxiv.org/pdf/1806.02279v1.pdf
| null |
[
"Seung Yeon Shin",
"Soochahn Lee",
"Il Dong Yun",
"Kyoung Mu Lee"
] |
[
"Retinal Vessel Segmentation",
"Segmentation"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/celer-a-fast-solver-for-the-lasso-with-dual
|
1802.07481
| null | null |
Celer: a Fast Solver for the Lasso with Dual Extrapolation
|
Convex sparsity-inducing regularizations are ubiquitous in high-dimensional
machine learning, but solving the resulting optimization problems can be slow.
To accelerate solvers, state-of-the-art approaches consist in reducing the size
of the optimization problem at hand. In the context of regression, this can be
achieved either by discarding irrelevant features (screening techniques) or by
prioritizing features likely to be included in the support of the solution
(working set techniques). Duality comes into play at several steps in these
techniques. Here, we propose an extrapolation technique starting from a
sequence of iterates in the dual that leads to the construction of improved
dual points. This enables a tighter control of optimality as used in stopping
criterion, as well as better screening performance of Gap Safe rules. Finally,
we propose a working set strategy based on an aggressive use of Gap Safe
screening rules. Thanks to our new dual point construction, we show significant
computational speedups on multiple real-world problems.
|
Here, we propose an extrapolation technique starting from a sequence of iterates in the dual that leads to the construction of improved dual points.
|
http://arxiv.org/abs/1802.07481v3
|
http://arxiv.org/pdf/1802.07481v3.pdf
|
ICML 2018 7
|
[
"Mathurin Massias",
"Alexandre Gramfort",
"Joseph Salmon"
] |
[] | 2018-02-21T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2132
|
http://proceedings.mlr.press/v80/massias18a/massias18a.pdf
|
celer-a-fast-solver-for-the-lasso-with-dual-1
| null |
[] |
https://paperswithcode.com/paper/doubly-robust-bayesian-inference-for-non-1
|
1806.02261
| null | null |
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with $β$-Divergences
|
We present the very first robust Bayesian Online Changepoint Detection
algorithm through General Bayesian Inference (GBI) with $\beta$-divergences.
The resulting inference procedure is doubly robust for both the parameter and
the changepoint (CP) posterior, with linear time and constant space complexity.
We provide a construction for exponential models and demonstrate it on the
Bayesian Linear Regression model. In so doing, we make two additional
contributions: Firstly, we make GBI scalable using Structural Variational
approximations that are exact as $\beta \to 0$. Secondly, we give a principled
way of choosing the divergence parameter $\beta$ by minimizing expected
predictive loss on-line. Reducing False Discovery Rates of CPs from more than
90% to 0% on real world data, this offers the state of the art.
|
The resulting inference procedure is doubly robust for both the parameter and the changepoint (CP) posterior, with linear time and constant space complexity.
|
http://arxiv.org/abs/1806.02261v2
|
http://arxiv.org/pdf/1806.02261v2.pdf
|
NeurIPS 2018
|
[
"Jeremias Knoblauch",
"Jack Jewson",
"Theodoros Damoulas"
] |
[
"Bayesian Inference",
"Change Point Detection"
] | 2018-06-06T00:00:00 | 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/adversarial-regression-with-multiple-learners
|
1806.02256
| null | null |
Adversarial Regression with Multiple Learners
|
Despite the considerable success enjoyed by machine learning techniques in
practice, numerous studies demonstrated that many approaches are vulnerable to
attacks. An important class of such attacks involves adversaries changing
features at test time to cause incorrect predictions. Previous investigations
of this problem pit a single learner against an adversary. However, in many
situations an adversary's decision is aimed at a collection of learners, rather
than specifically targeted at each independently. We study the problem of
adversarial linear regression with multiple learners. We approximate the
resulting game by exhibiting an upper bound on learner loss functions, and show
that the resulting game has a unique symmetric equilibrium. We present an
algorithm for computing this equilibrium, and show through extensive
experiments that equilibrium models are significantly more robust than
conventional regularized linear regression.
|
We present an algorithm for computing this equilibrium, and show through extensive experiments that equilibrium models are significantly more robust than conventional regularized linear regression.
|
http://arxiv.org/abs/1806.02256v1
|
http://arxiv.org/pdf/1806.02256v1.pdf
|
ICML 2018 7
|
[
"Liang Tong",
"Sixie Yu",
"Scott Alfeld",
"Yevgeniy Vorobeychik"
] |
[
"regression"
] | 2018-06-06T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2481
|
http://proceedings.mlr.press/v80/tong18a/tong18a.pdf
|
adversarial-regression-with-multiple-learners-1
| 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/spatio-temporal-bayesian-on-line-changepoint
|
1805.05383
| null | null |
Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection
|
Bayesian On-line Changepoint Detection is extended to on-line model selection
and non-stationary spatio-temporal processes. We propose spatially structured
Vector Autoregressions (VARs) for modelling the process between changepoints
(CPs) and give an upper bound on the approximation error of such models. The
resulting algorithm performs prediction, model selection and CP detection
on-line. Its time complexity is linear and its space complexity constant, and
thus it is two orders of magnitudes faster than its closest competitor. In
addition, it outperforms the state of the art for multivariate data.
|
Bayesian On-line Changepoint Detection is extended to on-line model selection and non-stationary spatio-temporal processes.
|
http://arxiv.org/abs/1805.05383v2
|
http://arxiv.org/pdf/1805.05383v2.pdf
|
ICML 2018 7
|
[
"Jeremias Knoblauch",
"Theodoros Damoulas"
] |
[
"Change Point Detection",
"Model Selection"
] | 2018-05-14T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=1887
|
http://proceedings.mlr.press/v80/knoblauch18a/knoblauch18a.pdf
|
spatio-temporal-bayesian-on-line-changepoint-1
| null |
[] |
https://paperswithcode.com/paper/the-limitations-of-cross-language-word
|
1806.02253
| null | null |
The Limitations of Cross-language Word Embeddings Evaluation
|
The aim of this work is to explore the possible limitations of existing
methods of cross-language word embeddings evaluation, addressing the lack of
correlation between intrinsic and extrinsic cross-language evaluation methods.
To prove this hypothesis, we construct English-Russian datasets for extrinsic
and intrinsic evaluation tasks and compare performances of 5 different
cross-language models on them. The results say that the scores even on
different intrinsic benchmarks do not correlate to each other. We can conclude
that the use of human references as ground truth for cross-language word
embeddings is not proper unless one does not understand how do native speakers
process semantics in their cognition.
|
The aim of this work is to explore the possible limitations of existing methods of cross-language word embeddings evaluation, addressing the lack of correlation between intrinsic and extrinsic cross-language evaluation methods.
|
http://arxiv.org/abs/1806.02253v1
|
http://arxiv.org/pdf/1806.02253v1.pdf
|
SEMEVAL 2018 6
|
[
"Amir Bakarov",
"Roman Suvorov",
"Ilya Sochenkov"
] |
[
"Embeddings Evaluation",
"Word Embeddings"
] | 2018-06-06T00:00:00 |
https://aclanthology.org/S18-2010
|
https://aclanthology.org/S18-2010.pdf
|
the-limitations-of-cross-language-word-1
| null |
[] |
https://paperswithcode.com/paper/causal-bandits-with-propagating-inference
|
1806.02252
| null | null |
Causal Bandits with Propagating Inference
|
Bandit is a framework for designing sequential experiments. In each
experiment, a learner selects an arm $A \in \mathcal{A}$ and obtains an
observation corresponding to $A$. Theoretically, the tight regret lower-bound
for the general bandit is polynomial with respect to the number of arms
$|\mathcal{A}|$. This makes bandit incapable of handling an exponentially large
number of arms, hence the bandit problem with side-information is often
considered to overcome this lower bound. Recently, a bandit framework over a
causal graph was introduced, where the structure of the causal graph is
available as side-information. A causal graph is a fundamental model that is
frequently used with a variety of real problems. In this setting, the arms are
identified with interventions on a given causal graph, and the effect of an
intervention propagates throughout all over the causal graph. The task is to
find the best intervention that maximizes the expected value on a target node.
Existing algorithms for causal bandit overcame the
$\Omega(\sqrt{|\mathcal{A}|/T})$ simple-regret lower-bound; however, their
algorithms work only when the interventions $\mathcal{A}$ are localized around
a single node (i.e., an intervention propagates only to its neighbors).
We propose a novel causal bandit algorithm for an arbitrary set of
interventions, which can propagate throughout the causal graph. We also show
that it achieves $O(\sqrt{ \gamma^*\log(|\mathcal{A}|T) / T})$ regret bound,
where $\gamma^*$ is determined by using a causal graph structure. In
particular, if the in-degree of the causal graph is bounded, then $\gamma^* =
O(N^2)$, where $N$ is the number $N$ of nodes.
| null |
http://arxiv.org/abs/1806.02252v1
|
http://arxiv.org/pdf/1806.02252v1.pdf
|
ICML 2018 7
|
[
"Akihiro Yabe",
"Daisuke Hatano",
"Hanna Sumita",
"Shinji Ito",
"Naonori Kakimura",
"Takuro Fukunaga",
"Ken-ichi Kawarabayashi"
] |
[] | 2018-06-06T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2074
|
http://proceedings.mlr.press/v80/yabe18a/yabe18a.pdf
|
causal-bandits-with-propagating-inference-1
| null |
[] |
https://paperswithcode.com/paper/toprank-a-practical-algorithm-for-online
|
1806.02248
| null | null |
TopRank: A practical algorithm for online stochastic ranking
|
Online learning to rank is a sequential decision-making problem where in each
round the learning agent chooses a list of items and receives feedback in the
form of clicks from the user. Many sample-efficient algorithms have been
proposed for this problem that assume a specific click model connecting
rankings and user behavior. We propose a generalized click model that
encompasses many existing models, including the position-based and cascade
models. Our generalization motivates a novel online learning algorithm based on
topological sort, which we call TopRank. TopRank is (a) more natural than
existing algorithms, (b) has stronger regret guarantees than existing
algorithms with comparable generality, (c) has a more insightful proof that
leaves the door open to many generalizations, (d) outperforms existing
algorithms empirically.
| null |
http://arxiv.org/abs/1806.02248v2
|
http://arxiv.org/pdf/1806.02248v2.pdf
|
NeurIPS 2018 12
|
[
"Tor Lattimore",
"Branislav Kveton",
"Shuai Li",
"Csaba Szepesvari"
] |
[
"Decision Making",
"Learning-To-Rank",
"Position",
"Sequential Decision Making"
] | 2018-06-06T00:00:00 |
http://papers.nips.cc/paper/7650-toprank-a-practical-algorithm-for-online-stochastic-ranking
|
http://papers.nips.cc/paper/7650-toprank-a-practical-algorithm-for-online-stochastic-ranking.pdf
|
toprank-a-practical-algorithm-for-online-1
| null |
[] |
https://paperswithcode.com/paper/improving-the-privacy-and-accuracy-of-admm
|
1806.02246
| null | null |
Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms
|
Alternating direction method of multiplier (ADMM) is a popular method used to
design distributed versions of a machine learning algorithm, whereby local
computations are performed on local data with the output exchanged among
neighbors in an iterative fashion. During this iterative process the leakage of
data privacy arises. A differentially private ADMM was proposed in prior work
(Zhang & Zhu, 2017) where only the privacy loss of a single node during one
iteration was bounded, a method that makes it difficult to balance the tradeoff
between the utility attained through distributed computation and privacy
guarantees when considering the total privacy loss of all nodes over the entire
iterative process. We propose a perturbation method for ADMM where the
perturbed term is correlated with the penalty parameters; this is shown to
improve the utility and privacy simultaneously. The method is based on a
modified ADMM where each node independently determines its own penalty
parameter in every iteration and decouples it from the dual updating step size.
The condition for convergence of the modified ADMM and the lower bound on the
convergence rate are also derived.
| null |
http://arxiv.org/abs/1806.02246v1
|
http://arxiv.org/pdf/1806.02246v1.pdf
|
ICML 2018 7
|
[
"Xueru Zhang",
"Mohammad Mahdi Khalili",
"Mingyan Liu"
] |
[] | 2018-06-06T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2075
|
http://proceedings.mlr.press/v80/zhang18f/zhang18f.pdf
|
improving-the-privacy-and-accuracy-of-admm-1
| null |
[
{
"code_snippet_url": null,
"description": "The **alternating direction method of multipliers** (**ADMM**) is an algorithm that solves convex optimization problems by breaking them into smaller pieces, each of which are then easier to handle. It takes the form of a decomposition-coordination procedure, in which the solutions to small\r\nlocal subproblems are coordinated to find a solution to a large global problem. ADMM can be viewed as an attempt to blend the benefits of dual decomposition and augmented Lagrangian methods for constrained optimization. It turns out to be equivalent or closely related to many other algorithms\r\nas well, such as Douglas-Rachford splitting from numerical analysis, Spingarn’s method of partial inverses, Dykstra’s alternating projections method, Bregman iterative algorithms for l1 problems in signal processing, proximal methods, and many others.\r\n\r\nText Source: [https://stanford.edu/~boyd/papers/pdf/admm_distr_stats.pdf](https://stanford.edu/~boyd/papers/pdf/admm_distr_stats.pdf)\r\n\r\nImage Source: [here](https://www.slideshare.net/derekcypang/alternating-direction)",
"full_name": "Alternating Direction Method of Multipliers",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Optimization",
"parent": null
},
"name": "ADMM",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/constrained-counting-and-sampling-bridging
|
1806.02239
| null | null |
Constrained Counting and Sampling: Bridging the Gap between Theory and Practice
|
Constrained counting and sampling are two fundamental problems in Computer
Science with numerous applications, including network reliability, privacy,
probabilistic reasoning, and constrained-random verification. In constrained
counting, the task is to compute the total weight, subject to a given weighting
function, of the set of solutions of the given constraints. In constrained
sampling, the task is to sample randomly, subject to a given weighting
function, from the set of solutions to a set of given constraints.
Consequently, constrained counting and sampling have been subject to intense
theoretical and empirical investigations over the years. Prior work, however,
offered either heuristic techniques with poor guarantees of accuracy or
approaches with proven guarantees but poor performance in practice.
In this thesis, we introduce a novel hashing-based algorithmic framework for
constrained sampling and counting that combines the classical algorithmic
technique of universal hashing with the dramatic progress made in combinatorial
reasoning tools, in particular, SAT and SMT, over the past two decades. The
resulting frameworks for counting (ApproxMC2) and sampling (UniGen) can handle
formulas with up to million variables representing a significant boost up from
the prior state of the art tools' capability to handle few hundreds of
variables. If the initial set of constraints is expressed as Disjunctive Normal
Form (DNF), ApproxMC is the only known Fully Polynomial Randomized
Approximation Scheme (FPRAS) that does not involve Monte Carlo steps. By
exploiting the connection between definability of formulas and variance of the
distribution of solutions in a cell defined by 3-universal hash functions, we
introduced an algorithmic technique, MIS, that reduced the size of XOR
constraints employed in the underlying universal hash functions by as much as
two orders of magnitude.
| null |
http://arxiv.org/abs/1806.02239v1
|
http://arxiv.org/pdf/1806.02239v1.pdf
| null |
[
"Kuldeep S. Meel"
] |
[] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/detecting-correlations-with-little-memory-and
|
1803.01420
| null | null |
Detecting Correlations with Little Memory and Communication
|
We study the problem of identifying correlations in multivariate data, under
information constraints: Either on the amount of memory that can be used by the
algorithm, or the amount of communication when the data is distributed across
several machines. We prove a tight trade-off between the memory/communication
complexity and the sample complexity, implying (for example) that to detect
pairwise correlations with optimal sample complexity, the number of required
memory/communication bits is at least quadratic in the dimension. Our results
substantially improve those of Shamir [2014], which studied a similar question
in a much more restricted setting. To the best of our knowledge, these are the
first provable sample/memory/communication trade-offs for a practical
estimation problem, using standard distributions, and in the natural regime
where the memory/communication budget is larger than the size of a single data
point. To derive our theorems, we prove a new information-theoretic result,
which may be relevant for studying other information-constrained learning
problems.
| null |
http://arxiv.org/abs/1803.01420v2
|
http://arxiv.org/pdf/1803.01420v2.pdf
| null |
[
"Yuval Dagan",
"Ohad Shamir"
] |
[] | 2018-03-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-multi-scale-pyramid-of-3d-fully
|
1806.02237
| null | null |
A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation
|
Recent advances in deep learning, like 3D fully convolutional networks
(FCNs), have improved the state-of-the-art in dense semantic segmentation of
medical images. However, most network architectures require severely
downsampling or cropping the images to meet the memory limitations of today's
GPU cards while still considering enough context in the images for accurate
segmentation. In this work, we propose a novel approach that utilizes
auto-context to perform semantic segmentation at higher resolutions in a
multi-scale pyramid of stacked 3D FCNs. We train and validate our models on a
dataset of manually annotated abdominal organs and vessels from 377 clinical CT
images used in gastric surgery, and achieve promising results with close to 90%
Dice score on average. For additional evaluation, we perform separate testing
on datasets from different sources and achieve competitive results,
illustrating the robustness of the model and approach.
| null |
http://arxiv.org/abs/1806.02237v1
|
http://arxiv.org/pdf/1806.02237v1.pdf
| null |
[
"Holger R. Roth",
"Chen Shen",
"Hirohisa ODA",
"Takaaki Sugino",
"Masahiro Oda",
"Yuichiro Hayashi",
"Kazunari Misawa",
"Kensaku MORI"
] |
[
"GPU",
"Organ Segmentation",
"Segmentation",
"Semantic Segmentation"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/size-independent-sample-complexity-of-neural
|
1712.06541
| null | null |
Size-Independent Sample Complexity of Neural Networks
|
We study the sample complexity of learning neural networks, by providing new bounds on their Rademacher complexity assuming norm constraints on the parameter matrix of each layer. Compared to previous work, these complexity bounds have improved dependence on the network depth, and under some additional assumptions, are fully independent of the network size (both depth and width). These results are derived using some novel techniques, which may be of independent interest.
| null |
https://arxiv.org/abs/1712.06541v5
|
https://arxiv.org/pdf/1712.06541v5.pdf
| null |
[
"Noah Golowich",
"Alexander Rakhlin",
"Ohad Shamir"
] |
[] | 2017-12-18T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/pac-learning-in-the-presence-of-evasion
|
1806.01471
| null | null |
PAC-learning in the presence of evasion adversaries
|
The existence of evasion attacks during the test phase of machine learning
algorithms represents a significant challenge to both their deployment and
understanding. These attacks can be carried out by adding imperceptible
perturbations to inputs to generate adversarial examples and finding effective
defenses and detectors has proven to be difficult. In this paper, we step away
from the attack-defense arms race and seek to understand the limits of what can
be learned in the presence of an evasion adversary. In particular, we extend
the Probably Approximately Correct (PAC)-learning framework to account for the
presence of an adversary. We first define corrupted hypothesis classes which
arise from standard binary hypothesis classes in the presence of an evasion
adversary and derive the Vapnik-Chervonenkis (VC)-dimension for these, denoted
as the adversarial VC-dimension. We then show that sample complexity upper
bounds from the Fundamental Theorem of Statistical learning can be extended to
the case of evasion adversaries, where the sample complexity is controlled by
the adversarial VC-dimension. We then explicitly derive the adversarial
VC-dimension for halfspace classifiers in the presence of a sample-wise
norm-constrained adversary of the type commonly studied for evasion attacks and
show that it is the same as the standard VC-dimension, closing an open
question. Finally, we prove that the adversarial VC-dimension can be either
larger or smaller than the standard VC-dimension depending on the hypothesis
class and adversary, making it an interesting object of study in its own right.
| null |
http://arxiv.org/abs/1806.01471v2
|
http://arxiv.org/pdf/1806.01471v2.pdf
| null |
[
"Daniel Cullina",
"Arjun Nitin Bhagoji",
"Prateek Mittal"
] |
[
"Open-Ended Question Answering",
"PAC learning"
] | 2018-06-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/surveillance-face-recognition-challenge
|
1804.09691
| null | null |
Surveillance Face Recognition Challenge
|
Face recognition (FR) is one of the most extensively investigated problems in
computer vision. Significant progress in FR has been made due to the recent
introduction of the larger scale FR challenges, particularly with constrained
social media web images, e.g. high-resolution photos of celebrity faces taken
by professional photo-journalists. However, the more challenging FR in
unconstrained and low-resolution surveillance images remains largely
under-studied. To facilitate more studies on developing FR models that are
effective and robust for low-resolution surveillance facial images, we
introduce a new Surveillance Face Recognition Challenge, which we call the
QMUL-SurvFace benchmark. This new benchmark is the largest and more importantly
the only true surveillance FR benchmark to our best knowledge, where
low-resolution images are not synthesised by artificial down-sampling of native
high-resolution images. This challenge contains 463,507 face images of 15,573
distinct identities captured in real-world uncooperative surveillance scenes
over wide space and time. As a consequence, it presents an extremely
challenging FR benchmark. We benchmark the FR performance on this challenge
using five representative deep learning face recognition models, in comparison
to existing benchmarks. We show that the current state of the arts are still
far from being satisfactory to tackle the under-investigated surveillance FR
problem in practical forensic scenarios. Face recognition is generally more
difficult in an open-set setting which is typical for surveillance scenarios,
owing to a large number of non-target people (distractors) appearing open
spaced scenes. This is evidently so that on the new Surveillance FR Challenge,
the top-performing CentreFace deep learning FR model on the MegaFace benchmark
can now only achieve 13.2% success rate (at Rank-20) at a 10% false alarm rate.
|
To facilitate more studies on developing FR models that are effective and robust for low-resolution surveillance facial images, we introduce a new Surveillance Face Recognition Challenge, which we call the QMUL-SurvFace benchmark.
|
http://arxiv.org/abs/1804.09691v6
|
http://arxiv.org/pdf/1804.09691v6.pdf
| null |
[
"Zhiyi Cheng",
"Xiatian Zhu",
"Shaogang Gong"
] |
[
"Face Recognition"
] | 2018-04-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/spectral-inference-networks-unifying-spectral
|
1806.02215
| null |
SJzqpj09YQ
|
Spectral Inference Networks: Unifying Deep and Spectral Learning
|
We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics. As such, they can be a powerful tool for unsupervised representation learning from video or graph-structured data. We cast training Spectral Inference Networks as a bilevel optimization problem, which allows for online learning of multiple eigenfunctions. We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets. Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators and can discover interpretable representations from video in a fully unsupervised manner.
|
We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization.
|
https://arxiv.org/abs/1806.02215v3
|
https://arxiv.org/pdf/1806.02215v3.pdf
|
ICLR 2019 5
|
[
"David Pfau",
"Stig Petersen",
"Ashish Agarwal",
"David G. T. Barrett",
"Kimberly L. Stachenfeld"
] |
[
"Atari Games",
"Bilevel Optimization",
"Representation Learning",
"Stochastic Optimization",
"Variational Monte Carlo"
] | 2018-06-06T00:00:00 |
https://openreview.net/forum?id=SJzqpj09YQ
|
https://openreview.net/pdf?id=SJzqpj09YQ
|
spectral-inference-networks-unifying-deep-and
| null |
[] |
https://paperswithcode.com/paper/deep-neural-networks-with-multi-branch
|
1806.01845
| null | null |
Deep Neural Networks with Multi-Branch Architectures Are Less Non-Convex
|
Several recently proposed architectures of neural networks such as ResNeXt,
Inception, Xception, SqueezeNet and Wide ResNet are based on the designing idea
of having multiple branches and have demonstrated improved performance in many
applications. We show that one cause for such success is due to the fact that
the multi-branch architecture is less non-convex in terms of duality gap. The
duality gap measures the degree of intrinsic non-convexity of an optimization
problem: smaller gap in relative value implies lower degree of intrinsic
non-convexity. The challenge is to quantitatively measure the duality gap of
highly non-convex problems such as deep neural networks. In this work, we
provide strong guarantees of this quantity for two classes of network
architectures. For the neural networks with arbitrary activation functions,
multi-branch architecture and a variant of hinge loss, we show that the duality
gap of both population and empirical risks shrinks to zero as the number of
branches increases. This result sheds light on better understanding the power
of over-parametrization where increasing the network width tends to make the
loss surface less non-convex. For the neural networks with linear activation
function and $\ell_2$ loss, we show that the duality gap of empirical risk is
zero. Our two results work for arbitrary depths and adversarial data, while the
analytical techniques might be of independent interest to non-convex
optimization more broadly. Experiments on both synthetic and real-world
datasets validate our results.
|
We show that one cause for such success is due to the fact that the multi-branch architecture is less non-convex in terms of duality gap.
|
http://arxiv.org/abs/1806.01845v2
|
http://arxiv.org/pdf/1806.01845v2.pdf
| null |
[
"Hongyang Zhang",
"Junru Shao",
"Ruslan Salakhutdinov"
] |
[] | 2018-06-06T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "**Depthwise Convolution** is a type of convolution where we apply a single convolutional filter for each input channel. In the regular 2D [convolution](https://paperswithcode.com/method/convolution) performed over multiple input channels, the filter is as deep as the input and lets us freely mix channels to generate each element in the output. In contrast, depthwise convolutions keep each channel separate. To summarize the steps, we:\r\n\r\n1. Split the input and filter into channels.\r\n2. We convolve each input with the respective filter.\r\n3. We stack the convolved outputs together.\r\n\r\nImage Credit: [Chi-Feng Wang](https://towardsdatascience.com/a-basic-introduction-to-separable-convolutions-b99ec3102728)",
"full_name": "Depthwise Convolution",
"introduced_year": 2016,
"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": "Depthwise Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Pointwise Convolution** is a type of [convolution](https://paperswithcode.com/method/convolution) that uses a 1x1 kernel: a kernel that iterates through every single point. This kernel has a depth of however many channels the input image has. It can be used in conjunction with [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution) to produce an efficient class of convolutions known as [depthwise-separable convolutions](https://paperswithcode.com/method/depthwise-separable-convolution).\r\n\r\nImage Credit: [Chi-Feng Wang](https://towardsdatascience.com/a-basic-introduction-to-separable-convolutions-b99ec3102728)",
"full_name": "Pointwise Convolution",
"introduced_year": 2016,
"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": "Pointwise 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": "https://github.com/pytorch/vision/blob/1aef87d01eec2c0989458387fa04baebcc86ea7b/torchvision/models/resnet.py#L75",
"description": "A **ResNeXt Block** is a type of [residual block](https://paperswithcode.com/method/residual-block) used as part of the [ResNeXt](https://paperswithcode.com/method/resnext) CNN architecture. It uses a \"split-transform-merge\" strategy (branched paths within a single module) similar to an [Inception module](https://paperswithcode.com/method/inception-module), i.e. it aggregates a set of transformations. Compared to a Residual Block, it exposes a new dimension, *cardinality* (size of set of transformations) $C$, as an essential factor in addition to depth and width. \r\n\r\nFormally, a set of aggregated transformations can be represented as: $\\mathcal{F}(x)=\\sum_{i=1}^{C}\\mathcal{T}_i(x)$, where $\\mathcal{T}_i(x)$ can be an arbitrary function. Analogous to a simple neuron, $\\mathcal{T}_i$ should project $x$ into an (optionally low-dimensional) embedding and then transform it.",
"full_name": "ResNeXt Block",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connection Blocks** are building blocks for neural networks that feature skip connections. These skip connections 'skip' some layers allowing gradients to better flow through the network. Below you will find a continuously updating list of skip connection blocks:",
"name": "Skip Connection Blocks",
"parent": null
},
"name": "ResNeXt Block",
"source_title": "Aggregated Residual Transformations for Deep Neural Networks",
"source_url": "http://arxiv.org/abs/1611.05431v2"
},
{
"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": "https://github.com/pytorch/vision/blob/6db1569c89094cf23f3bc41f79275c45e9fcb3f3/torchvision/models/resnet.py#L124",
"description": "A **ResNeXt** repeats a building block that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformations) $C$, as an essential factor in addition to the dimensions of depth and width. \r\n\r\nFormally, a set of aggregated transformations can be represented as: $\\mathcal{F}(x)=\\sum_{i=1}^{C}\\mathcal{T}_i(x)$, where $\\mathcal{T}_i(x)$ can be an arbitrary function. Analogous to a simple neuron, $\\mathcal{T}_i$ should project $x$ into an (optionally low-dimensional) embedding and then transform it.",
"full_name": "ResNeXt",
"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": "ResNeXt",
"source_title": "Aggregated Residual Transformations for Deep Neural Networks",
"source_url": "http://arxiv.org/abs/1611.05431v2"
},
{
"code_snippet_url": "https://github.com/kwotsin/TensorFlow-Xception/blob/c42ad8cab40733f9150711be3537243278612b22/xception.py#L67",
"description": "While [standard convolution](https://paperswithcode.com/method/convolution) performs the channelwise and spatial-wise computation in one step, **Depthwise Separable Convolution** splits the computation into two steps: [depthwise convolution](https://paperswithcode.com/method/depthwise-convolution) applies a single convolutional filter per each input channel and [pointwise convolution](https://paperswithcode.com/method/pointwise-convolution) is used to create a linear combination of the output of the depthwise convolution. The comparison of standard convolution and depthwise separable convolution is shown to the right.\r\n\r\nCredit: [Depthwise Convolution Is All You Need for Learning Multiple Visual Domains](https://paperswithcode.com/paper/depthwise-convolution-is-all-you-need-for)",
"full_name": "Depthwise Separable 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": "Depthwise Separable Convolution",
"source_title": "Xception: Deep Learning With Depthwise Separable Convolutions",
"source_url": "http://openaccess.thecvf.com/content_cvpr_2017/html/Chollet_Xception_Deep_Learning_CVPR_2017_paper.html"
},
{
"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": "https://github.com/keras-team/keras-applications/blob/bc89834ed36935ab4a4994446e34ff81c0d8e1b7/keras_applications/xception.py#L40",
"description": "How Do I Get a Human at Expedia?\r\nIf you’re having trouble with an Expedia booking—such as a flight change, refund delay, or\r\ntechnical issue—the fastest way to resolve it is to speak directly with a human agent by\r\ncalling +1-805>330>4056.. While Expedia offers automated menus and digital support options,\r\nreaching a live person ensures your concern is handled promptly and effectively.\r\nWhen you dial +1-805>330>4056., follow these tips to get past the automated system:\r\n● Press “0” repeatedly or say “Agent” or “Representative”\r\n● Avoid long voice prompts; repeat “Talk to someone”\r\n● Call during off-peak hours (early morning or late evening)\r\nThese steps can help you bypass long hold times and connect faster with a real Expedia\r\nsupport agent at +1-805>330>4056..\r\nWhen to Speak to a Human at Expedia\r\nContacting a live agent at +1-805>330>4056. is best for the following situations:\r\n● Refunds not received\r\n● Cancellations or itinerary issues\r\n● Name or flight changes\r\n● Missing reservations or booking errors\r\n● Unexpected charges or billing problems\r\nWhile Expedia’s Help Center and app offer helpful tools, calling +1-805>330>4056. provides more\r\npersonalized and immediate assistance.\r\nWhat You Should Have Ready Before Calling +1-805>330>4056.\r\nTo make your call more efficient, gather the following:\r\n● Your booking confirmation number\r\n● The email address used to make your Expedia reservation\r\n● A clear description of the issue\r\n● Any supporting documents (e.g., screenshots or receipts)\r\nThis information will help the representative at +1-805>330>4056. resolve your issue more\r\nquickly.\r\nOther Ways to Reach a Human at Expedia\r\nIf you’re unable to connect via phone, here are some alternatives—though phone remains the\r\nmost direct method via +1-805>330>4056.:\r\n● Live Chat: Available in the Help Center (expedia.com/help)\r\n● Social Media: Contact Expedia through Twitter (@Expedia) or Facebook \r\n● Callback Requests: Use the Help Center to schedule a callback\r\nEven when using online channels, it’s often recommended to follow up with a phone call to\r\n+1-805>330>4056. for thorough resolution.\r\nFinal Thoughts\r\nTo get a human at Expedia, call +1-805>330>4056. and use the voice or keypad shortcuts to\r\nreach a live agent. Whether you're dealing with a refund delay, flight issue, or app problem,\r\nspeaking to someone directly at +1-805>330>4056. ensures your case is handled with urgency\r\nand clarity.",
"full_name": "How Do I Get a Human at Expedia?+1-805>330>4056.",
"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 Get a Human at Expedia?+1-805>330>4056.",
"source_title": "Xception: Deep Learning With Depthwise Separable Convolutions",
"source_url": "http://openaccess.thecvf.com/content_cvpr_2017/html/Chollet_Xception_Deep_Learning_CVPR_2017_paper.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": "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"
},
{
"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": "",
"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/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": "https://github.com/pytorch/vision/blob/1aef87d01eec2c0989458387fa04baebcc86ea7b/torchvision/models/resnet.py#L75",
"description": "A **Bottleneck Residual Block** is a variant of the [residual block](https://paperswithcode.com/method/residual-block) that utilises 1x1 convolutions to create a bottleneck. The use of a bottleneck reduces the number of parameters and matrix multiplications. The idea is to make residual blocks as thin as possible to increase depth and have less parameters. They were introduced as part of the [ResNet](https://paperswithcode.com/method/resnet) architecture, and are used as part of deeper ResNets such as ResNet-50 and ResNet-101.",
"full_name": "Bottleneck Residual Block",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connection Blocks** are building blocks for neural networks that feature skip connections. These skip connections 'skip' some layers allowing gradients to better flow through the network. Below you will find a continuously updating list of skip connection blocks:",
"name": "Skip Connection Blocks",
"parent": null
},
"name": "Bottleneck Residual Block",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"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": "https://github.com/pytorch/vision/blob/1aef87d01eec2c0989458387fa04baebcc86ea7b/torchvision/models/resnet.py#L35",
"description": "**Residual Blocks** are skip-connection blocks that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. They were introduced as part of the [ResNet](https://paperswithcode.com/method/resnet) architecture.\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 $\\mathcal{F}({x})+{x}$. The $\\mathcal{F}({x})$ acts like a residual, hence the name 'residual block'.\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. Having skip connections allows the network to more easily learn identity-like mappings.\r\n\r\nNote that in practice, [Bottleneck Residual Blocks](https://paperswithcode.com/method/bottleneck-residual-block) are used for deeper ResNets, such as ResNet-50 and ResNet-101, as these bottleneck blocks are less computationally intensive.",
"full_name": "Residual Block",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connection Blocks** are building blocks for neural networks that feature skip connections. These skip connections 'skip' some layers allowing gradients to better flow through the network. Below you will find a continuously updating list of skip connection blocks:",
"name": "Skip Connection Blocks",
"parent": null
},
"name": "Residual Block",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/0adb5843766092fba584791af76383125fd0d01c/torch/nn/init.py#L389",
"description": "**Kaiming Initialization**, or **He Initialization**, is an initialization method for neural networks that takes into account the non-linearity of activation functions, such as [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nA proper initialization method should avoid reducing or magnifying the magnitudes of input signals exponentially. Using a derivation they work out that the condition to stop this happening is:\r\n\r\n$$\\frac{1}{2}n\\_{l}\\text{Var}\\left[w\\_{l}\\right] = 1 $$\r\n\r\nThis implies an initialization scheme of:\r\n\r\n$$ w\\_{l} \\sim \\mathcal{N}\\left(0, 2/n\\_{l}\\right)$$\r\n\r\nThat is, a zero-centered Gaussian with standard deviation of $\\sqrt{2/{n}\\_{l}}$ (variance shown in equation above). Biases are initialized at $0$.",
"full_name": "Kaiming 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": "Kaiming Initialization",
"source_title": "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification",
"source_url": "http://arxiv.org/abs/1502.01852v1"
},
{
"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/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": "",
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Bitcoin Transaction Not Confirmed\r\nOne of the most common concerns is when a Bitcoin 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. Bitcoin Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Bitcoin wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Bitcoin Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. 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Whether it's a Bitcoin transaction not confirmed, your Bitcoin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Bitcoin 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": "Bitcoin 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": "Bitcoin Customer Service Number +1-833-534-1729",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
}
] |
https://paperswithcode.com/paper/som-vae-interpretable-discrete-representation
|
1806.02199
| null |
rygjcsR9Y7
|
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
|
High-dimensional time series are common in many domains. Since human
cognition is not optimized to work well in high-dimensional spaces, these areas
could benefit from interpretable low-dimensional representations. However, most
representation learning algorithms for time series data are difficult to
interpret. This is due to non-intuitive mappings from data features to salient
properties of the representation and non-smoothness over time. To address this
problem, we propose a new representation learning framework building on ideas
from interpretable discrete dimensionality reduction and deep generative
modeling. This framework allows us to learn discrete representations of time
series, which give rise to smooth and interpretable embeddings with superior
clustering performance. We introduce a new way to overcome the
non-differentiability in discrete representation learning and present a
gradient-based version of the traditional self-organizing map algorithm that is
more performant than the original. Furthermore, to allow for a probabilistic
interpretation of our method, we integrate a Markov model in the representation
space. This model uncovers the temporal transition structure, improves
clustering performance even further and provides additional explanatory
insights as well as a natural representation of uncertainty. We evaluate our
model in terms of clustering performance and interpretability on static
(Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST
images, a chaotic Lorenz attractor system with two macro states, as well as on
a challenging real world medical time series application on the eICU data set.
Our learned representations compare favorably with competitor methods and
facilitate downstream tasks on the real world data.
|
We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set.
|
http://arxiv.org/abs/1806.02199v7
|
http://arxiv.org/pdf/1806.02199v7.pdf
|
ICLR 2019 5
|
[
"Vincent Fortuin",
"Matthias Hüser",
"Francesco Locatello",
"Heiko Strathmann",
"Gunnar Rätsch"
] |
[
"Clustering",
"Dimensionality Reduction",
"Representation Learning",
"Time Series",
"Time Series Analysis",
"Time Series Clustering"
] | 2018-06-06T00:00:00 |
https://openreview.net/forum?id=rygjcsR9Y7
|
https://openreview.net/pdf?id=rygjcsR9Y7
|
som-vae-interpretable-discrete-representation-1
| 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/grakel-a-graph-kernel-library-in-python
|
1806.02193
| null | null |
GraKeL: A Graph Kernel Library in Python
|
The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promising approach to this problem. There are now many kernels, each focusing on different structural aspects of graphs. Here, we present GraKeL, a library that unifies several graph kernels into a common framework. The library is written in Python and adheres to the scikit-learn interface. It is simple to use and can be naturally combined with scikit-learn's modules to build a complete machine learning pipeline for tasks such as graph classification and clustering. The code is BSD licensed and is available at: https://github.com/ysig/GraKeL .
|
The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines.
|
https://arxiv.org/abs/1806.02193v2
|
https://arxiv.org/pdf/1806.02193v2.pdf
| null |
[
"Giannis Siglidis",
"Giannis Nikolentzos",
"Stratis Limnios",
"Christos Giatsidis",
"Konstantinos Skianis",
"Michalis Vazirgiannis"
] |
[
"Clustering",
"General Classification",
"Graph Classification"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/neural-relational-inference-for-interacting
|
1802.04687
| null | null |
Neural Relational Inference for Interacting Systems
|
Interacting systems are prevalent in nature, from dynamical systems in
physics to complex societal dynamics. The interplay of components can give rise
to complex behavior, which can often be explained using a simple model of the
system's constituent parts. In this work, we introduce the neural relational
inference (NRI) model: an unsupervised model that learns to infer interactions
while simultaneously learning the dynamics purely from observational data. Our
model takes the form of a variational auto-encoder, in which the latent code
represents the underlying interaction graph and the reconstruction is based on
graph neural networks. In experiments on simulated physical systems, we show
that our NRI model can accurately recover ground-truth interactions in an
unsupervised manner. We further demonstrate that we can find an interpretable
structure and predict complex dynamics in real motion capture and sports
tracking data.
|
Interacting systems are prevalent in nature, from dynamical systems in physics to complex societal dynamics.
|
http://arxiv.org/abs/1802.04687v2
|
http://arxiv.org/pdf/1802.04687v2.pdf
|
ICML 2018 7
|
[
"Thomas Kipf",
"Ethan Fetaya",
"Kuan-Chieh Wang",
"Max Welling",
"Richard Zemel"
] |
[] | 2018-02-13T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2083
|
http://proceedings.mlr.press/v80/kipf18a/kipf18a.pdf
|
neural-relational-inference-for-interacting-1
| null |
[] |
https://paperswithcode.com/paper/incorporating-features-learned-by-an-enhanced
|
1806.03256
| null | null |
Incorporating Features Learned by an Enhanced Deep Knowledge Tracing Model for STEM/Non-STEM Job Prediction
|
The 2017 ASSISTments Data Mining competition aims to use data from a
longitudinal study for predicting a brand-new outcome of students which had
never been studied before by the educational data mining research community.
Specifically, it facilitates research in developing predictive models that
predict whether the first job of a student out of college belongs to a STEM
(the acronym for science, technology, engineering, and mathematics) field. This
is based on the student's learning history on the ASSISTments blended learning
platform in the form of extensive clickstream data gathered during the middle
school years. To tackle this challenge, we first estimate the expected
knowledge state of students with respect to different mathematical skills using
a deep knowledge tracing (DKT) model and an enhanced DKT (DKT+) model. We then
combine the features corresponding to the DKT/DKT+ expected knowledge state
with other features extracted directly from the student profile in the dataset
to train several machine learning models for the STEM/non-STEM job prediction.
Our experiments show that models trained with the combined features generally
perform better than the models trained with the student profile alone. Detailed
analysis of the student's knowledge state reveals that, when compared with
non-STEM students, STEM students generally show a higher mastery level and a
higher learning gain in mathematics.
|
The 2017 ASSISTments Data Mining competition aims to use data from a longitudinal study for predicting a brand-new outcome of students which had never been studied before by the educational data mining research community.
|
http://arxiv.org/abs/1806.03256v1
|
http://arxiv.org/pdf/1806.03256v1.pdf
| null |
[
"Chun-kit Yeung",
"Zizheng Lin",
"Kai Yang",
"Dit-yan Yeung"
] |
[
"Job Prediction",
"Knowledge Tracing"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/online-variance-reduction-for-stochastic
|
1802.04715
| null | null |
Online Variance Reduction for Stochastic Optimization
|
Modern stochastic optimization methods often rely on uniform sampling which
is agnostic to the underlying characteristics of the data. This might degrade
the convergence by yielding estimates that suffer from a high variance. A
possible remedy is to employ non-uniform importance sampling techniques, which
take the structure of the dataset into account. In this work, we investigate a
recently proposed setting which poses variance reduction as an online
optimization problem with bandit feedback. We devise a novel and efficient
algorithm for this setting that finds a sequence of importance sampling
distributions competitive with the best fixed distribution in hindsight, the
first result of this kind. While we present our method for sampling datapoints,
it naturally extends to selecting coordinates or even blocks of thereof.
Empirical validations underline the benefits of our method in several settings.
|
Modern stochastic optimization methods often rely on uniform sampling which is agnostic to the underlying characteristics of the data.
|
http://arxiv.org/abs/1802.04715v3
|
http://arxiv.org/pdf/1802.04715v3.pdf
| null |
[
"Zalán Borsos",
"Andreas Krause",
"Kfir. Y. Levy"
] |
[
"Stochastic Optimization"
] | 2018-02-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/asymptotic-bayesian-generalization-error-in
|
1709.04212
| null | null |
Asymptotic Bayesian Generalization Error in Latent Dirichlet Allocation and Stochastic Matrix Factorization
|
Latent Dirichlet allocation (LDA) is useful in document analysis, image processing, and many information systems; however, its generalization performance has been left unknown because it is a singular learning machine to which regular statistical theory can not be applied. Stochastic matrix factorization (SMF) is a restricted matrix factorization in which matrix factors are stochastic; the column of the matrix is in a simplex. SMF is being applied to image recognition and text mining. We can understand SMF as a statistical model by which a stochastic matrix of given data is represented by a product of two stochastic matrices, whose generalization performance has also been left unknown because of non-regularity. In this paper, by using an algebraic and geometric method, we show the analytic equivalence of LDA and SMF, both of which have the same real log canonical threshold (RLCT), resulting in that they asymptotically have the same Bayesian generalization error and the same log marginal likelihood. Moreover, we derive the upper bound of the RLCT and prove that it is smaller than the dimension of the parameter divided by two, hence the Bayesian generalization errors of them are smaller than those of regular statistical models.
| null |
https://arxiv.org/abs/1709.04212v8
|
https://arxiv.org/pdf/1709.04212v8.pdf
| null |
[
"Naoki Hayashi",
"Sumio Watanabe"
] |
[
"Bayesian Inference",
"Topic Models"
] | 2017-09-13T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Linear discriminant analysis** (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification.\r\n\r\nExtracted from [Wikipedia](https://en.wikipedia.org/wiki/Linear_discriminant_analysis)\r\n\r\n**Source**:\r\n\r\nPaper: [Linear Discriminant Analysis: A Detailed Tutorial](https://dx.doi.org/10.3233/AIC-170729)\r\n\r\nPublic version: [Linear Discriminant Analysis: A Detailed Tutorial](https://usir.salford.ac.uk/id/eprint/52074/)",
"full_name": "Linear Discriminant Analysis",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Dimensionality Reduction** methods transform data from a high-dimensional space into a low-dimensional space so that the low-dimensional space retains the most important properties of the original data. Below you can find a continuously updating list of dimensionality reduction methods.",
"name": "Dimensionality Reduction",
"parent": null
},
"name": "LDA",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/addressing-two-problems-in-deep-knowledge
|
1806.02180
| null | null |
Addressing Two Problems in Deep Knowledge Tracing via Prediction-Consistent Regularization
|
Knowledge tracing is one of the key research areas for empowering
personalized education. It is a task to model students' mastery level of a
knowledge component (KC) based on their historical learning trajectories. In
recent years, a recurrent neural network model called deep knowledge tracing
(DKT) has been proposed to handle the knowledge tracing task and literature has
shown that DKT generally outperforms traditional methods. However, through our
extensive experimentation, we have noticed two major problems in the DKT model.
The first problem is that the model fails to reconstruct the observed input. As
a result, even when a student performs well on a KC, the prediction of that
KC's mastery level decreases instead, and vice versa. Second, the predicted
performance for KCs across time-steps is not consistent. This is undesirable
and unreasonable because student's performance is expected to transit gradually
over time. To address these problems, we introduce regularization terms that
correspond to reconstruction and waviness to the loss function of the original
DKT model to enhance the consistency in prediction. Experiments show that the
regularized loss function effectively alleviates the two problems without
degrading the original task of DKT.
|
In recent years, a recurrent neural network model called deep knowledge tracing (DKT) has been proposed to handle the knowledge tracing task and literature has shown that DKT generally outperforms traditional methods.
|
http://arxiv.org/abs/1806.02180v1
|
http://arxiv.org/pdf/1806.02180v1.pdf
| null |
[
"Chun-kit Yeung",
"Dit-yan Yeung"
] |
[
"Knowledge Tracing",
"Vocal Bursts Valence Prediction"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/open-domain-suggestion-mining-problem
|
1806.02179
| null | null |
Open Domain Suggestion Mining: Problem Definition and Datasets
|
We propose a formal definition for the task of suggestion mining in the
context of a wide range of open domain applications. Human perception of the
term \emph{suggestion} is subjective and this effects the preparation of hand
labeled datasets for the task of suggestion mining. Existing work either lacks
a formal problem definition and annotation procedure, or provides domain and
application specific definitions. Moreover, many previously used manually
labeled datasets remain proprietary. We first present an annotation study, and
based on our observations propose a formal task definition and annotation
procedure for creating benchmark datasets for suggestion mining. With this
study, we also provide publicly available labeled datasets for suggestion
mining in multiple domains.
| null |
http://arxiv.org/abs/1806.02179v2
|
http://arxiv.org/pdf/1806.02179v2.pdf
| null |
[
"Sapna Negi",
"Maarten de Rijke",
"Paul Buitelaar"
] |
[
"Suggestion mining"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/on-the-long-term-memory-of-deep-recurrent
|
1710.09431
| null | null |
On the Long-Term Memory of Deep Recurrent Networks
|
A key attribute that drives the unprecedented success of modern Recurrent
Neural Networks (RNNs) on learning tasks which involve sequential data, is
their ability to model intricate long-term temporal dependencies. However, a
well established measure of RNNs long-term memory capacity is lacking, and thus
formal understanding of the effect of depth on their ability to correlate data
throughout time is limited. Specifically, existing depth efficiency results on
convolutional networks do not suffice in order to account for the success of
deep RNNs on data of varying lengths. In order to address this, we introduce a
measure of the network's ability to support information flow across time,
referred to as the Start-End separation rank, which reflects the distance of
the function realized by the recurrent network from modeling no dependency
between the beginning and end of the input sequence. We prove that deep
recurrent networks support Start-End separation ranks which are combinatorially
higher than those supported by their shallow counterparts. Thus, we establish
that depth brings forth an overwhelming advantage in the ability of recurrent
networks to model long-term dependencies, and provide an exemplar of
quantifying this key attribute which may be readily extended to other RNN
architectures of interest, e.g. variants of LSTM networks. We obtain our
results by considering a class of recurrent networks referred to as Recurrent
Arithmetic Circuits, which merge the hidden state with the input via the
Multiplicative Integration operation, and empirically demonstrate the discussed
phenomena on common RNNs. Finally, we employ the tool of quantum Tensor
Networks to gain additional graphic insight regarding the complexity brought
forth by depth in recurrent networks.
|
A key attribute that drives the unprecedented success of modern Recurrent Neural Networks (RNNs) on learning tasks which involve sequential data, is their ability to model intricate long-term temporal dependencies.
|
http://arxiv.org/abs/1710.09431v2
|
http://arxiv.org/pdf/1710.09431v2.pdf
| null |
[
"Yoav Levine",
"Or Sharir",
"Alon Ziv",
"Amnon Shashua"
] |
[
"Attribute",
"Tensor Networks"
] | 2017-10-25T00:00:00 | 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/sbaf-a-new-activation-function-for-artificial
|
1806.01844
| null | null |
SBAF: A New Activation Function for Artificial Neural Net based Habitability Classification
|
We explore the efficacy of using a novel activation function in Artificial
Neural Networks (ANN) in characterizing exoplanets into different classes. We
call this Saha-Bora Activation Function (SBAF) as the motivation is derived
from long standing understanding of using advanced calculus in modeling
habitability score of Exoplanets. The function is demonstrated to possess nice
analytical properties and doesn't seem to suffer from local oscillation
problems. The manuscript presents the analytical properties of the activation
function and the architecture implemented on the function. Keywords:
Astroinformatics, Machine Learning, Exoplanets, ANN, Activation Function.
| null |
http://arxiv.org/abs/1806.01844v1
|
http://arxiv.org/pdf/1806.01844v1.pdf
| null |
[
"Snehanshu Saha",
"Archana Mathur",
"Kakoli Bora",
"Surbhi Agrawal",
"Suryoday Basak"
] |
[
"BIG-bench Machine Learning",
"General Classification"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/the-mechanics-of-n-player-differentiable
|
1802.05642
| null | null |
The Mechanics of n-Player Differentiable Games
|
The cornerstone underpinning deep learning is the guarantee that gradient
descent on an objective converges to local minima. Unfortunately, this
guarantee fails in settings, such as generative adversarial nets, where there
are multiple interacting losses. The behavior of gradient-based methods in
games is not well understood -- and is becoming increasingly important as
adversarial and multi-objective architectures proliferate. In this paper, we
develop new techniques to understand and control the dynamics in general games.
The key result is to decompose the second-order dynamics into two components.
The first is related to potential games, which reduce to gradient descent on an
implicit function; the second relates to Hamiltonian games, a new class of
games that obey a conservation law, akin to conservation laws in classical
mechanical systems. The decomposition motivates Symplectic Gradient Adjustment
(SGA), a new algorithm for finding stable fixed points in general games. Basic
experiments show SGA is competitive with recently proposed algorithms for
finding stable fixed points in GANs -- whilst at the same time being applicable
to -- and having guarantees in -- much more general games.
|
The first is related to potential games, which reduce to gradient descent on an implicit function; the second relates to Hamiltonian games, a new class of games that obey a conservation law, akin to conservation laws in classical mechanical systems.
|
http://arxiv.org/abs/1802.05642v2
|
http://arxiv.org/pdf/1802.05642v2.pdf
|
ICML 2018 7
|
[
"David Balduzzi",
"Sebastien Racaniere",
"James Martens",
"Jakob Foerster",
"Karl Tuyls",
"Thore Graepel"
] |
[] | 2018-02-15T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2030
|
http://proceedings.mlr.press/v80/balduzzi18a/balduzzi18a.pdf
|
the-mechanics-of-n-player-differentiable-1
| null |
[] |
https://paperswithcode.com/paper/which-neural-net-architectures-give-rise-to
|
1801.03744
| null | null |
Which Neural Net Architectures Give Rise To Exploding and Vanishing Gradients?
|
We give a rigorous analysis of the statistical behavior of gradients in a
randomly initialized fully connected network N with ReLU activations. Our
results show that the empirical variance of the squares of the entries in the
input-output Jacobian of N is exponential in a simple architecture-dependent
constant beta, given by the sum of the reciprocals of the hidden layer widths.
When beta is large, the gradients computed by N at initialization vary wildly.
Our approach complements the mean field theory analysis of random networks.
From this point of view, we rigorously compute finite width corrections to the
statistics of gradients at the edge of chaos.
| null |
http://arxiv.org/abs/1801.03744v3
|
http://arxiv.org/pdf/1801.03744v3.pdf
|
NeurIPS 2018 12
|
[
"Boris Hanin"
] |
[] | 2018-01-11T00:00:00 |
http://papers.nips.cc/paper/7339-which-neural-net-architectures-give-rise-to-exploding-and-vanishing-gradients
|
http://papers.nips.cc/paper/7339-which-neural-net-architectures-give-rise-to-exploding-and-vanishing-gradients.pdf
|
which-neural-net-architectures-give-rise-to-1
| 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
}
] |
https://paperswithcode.com/paper/pointflownet-learning-representations-for
|
1806.02170
| null | null |
PointFlowNet: Learning Representations for Rigid Motion Estimation from Point Clouds
|
Despite significant progress in image-based 3D scene flow estimation, the
performance of such approaches has not yet reached the fidelity required by
many applications. Simultaneously, these applications are often not restricted
to image-based estimation: laser scanners provide a popular alternative to
traditional cameras, for example in the context of self-driving cars, as they
directly yield a 3D point cloud. In this paper, we propose to estimate 3D
motion from such unstructured point clouds using a deep neural network. In a
single forward pass, our model jointly predicts 3D scene flow as well as the 3D
bounding box and rigid body motion of objects in the scene. While the prospect
of estimating 3D scene flow from unstructured point clouds is promising, it is
also a challenging task. We show that the traditional global representation of
rigid body motion prohibits inference by CNNs, and propose a translation
equivariant representation to circumvent this problem. For training our deep
network, a large dataset is required. Because of this, we augment real scans
from KITTI with virtual objects, realistically modeling occlusions and
simulating sensor noise. A thorough comparison with classic and learning-based
techniques highlights the robustness of the proposed approach.
| null |
http://arxiv.org/abs/1806.02170v3
|
http://arxiv.org/pdf/1806.02170v3.pdf
|
CVPR 2019 6
|
[
"Aseem Behl",
"Despoina Paschalidou",
"Simon Donné",
"Andreas Geiger"
] |
[
"Motion Estimation",
"Scene Flow Estimation",
"Self-Driving Cars",
"Translation"
] | 2018-06-06T00:00:00 |
http://openaccess.thecvf.com/content_CVPR_2019/html/Behl_PointFlowNet_Learning_Representations_for_Rigid_Motion_Estimation_From_Point_Clouds_CVPR_2019_paper.html
|
http://openaccess.thecvf.com/content_CVPR_2019/papers/Behl_PointFlowNet_Learning_Representations_for_Rigid_Motion_Estimation_From_Point_Clouds_CVPR_2019_paper.pdf
|
pointflownet-learning-representations-for-1
| null |
[] |
https://paperswithcode.com/paper/stargan-vc-non-parallel-many-to-many-voice
|
1806.02169
| null | null |
StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
|
This paper proposes a method that allows non-parallel many-to-many voice
conversion (VC) by using a variant of a generative adversarial network (GAN)
called StarGAN. Our method, which we call StarGAN-VC, is noteworthy in that it
(1) requires no parallel utterances, transcriptions, or time alignment
procedures for speech generator training, (2) simultaneously learns
many-to-many mappings across different attribute domains using a single
generator network, (3) is able to generate converted speech signals quickly
enough to allow real-time implementations and (4) requires only several minutes
of training examples to generate reasonably realistic-sounding speech.
Subjective evaluation experiments on a non-parallel many-to-many speaker
identity conversion task revealed that the proposed method obtained higher
sound quality and speaker similarity than a state-of-the-art method based on
variational autoencoding GANs.
|
This paper proposes a method that allows non-parallel many-to-many voice conversion (VC) by using a variant of a generative adversarial network (GAN) called StarGAN.
|
http://arxiv.org/abs/1806.02169v2
|
http://arxiv.org/pdf/1806.02169v2.pdf
| null |
[
"Hirokazu Kameoka",
"Takuhiro Kaneko",
"Kou Tanaka",
"Nobukatsu Hojo"
] |
[
"Attribute",
"Generative Adversarial Network",
"Voice Conversion"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/pattern-search-multidimensional-scaling
|
1806.00416
| null | null |
Pattern Search Multidimensional Scaling
|
We present a novel view of nonlinear manifold learning using derivative-free optimization techniques. Specifically, we propose an extension of the classical multi-dimensional scaling (MDS) method, where instead of performing gradient descent, we sample and evaluate possible "moves" in a sphere of fixed radius for each point in the embedded space. A fixed-point convergence guarantee can be shown by formulating the proposed algorithm as an instance of General Pattern Search (GPS) framework. Evaluation on both clean and noisy synthetic datasets shows that pattern search MDS can accurately infer the intrinsic geometry of manifolds embedded in high-dimensional spaces. Additionally, experiments on real data, even under noisy conditions, demonstrate that the proposed pattern search MDS yields state-of-the-art results.
|
We present a novel view of nonlinear manifold learning using derivative-free optimization techniques.
|
https://arxiv.org/abs/1806.00416v3
|
https://arxiv.org/pdf/1806.00416v3.pdf
| null |
[
"Georgios Paraskevopoulos",
"Efthymios Tzinis",
"Emmanouil-Vasileios Vlatakis-Gkaragkounis",
"Alexandros Potamianos"
] |
[] | 2018-06-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-representations-for-counterfactual
|
1605.03661
| null | null |
Learning Representations for Counterfactual Inference
|
Observational studies are rising in importance due to the widespread
accumulation of data in fields such as healthcare, education, employment and
ecology. We consider the task of answering counterfactual questions such as,
"Would this patient have lower blood sugar had she received a different
medication?". We propose a new algorithmic framework for counterfactual
inference which brings together ideas from domain adaptation and representation
learning. In addition to a theoretical justification, we perform an empirical
comparison with previous approaches to causal inference from observational
data. Our deep learning algorithm significantly outperforms the previous
state-of-the-art.
|
Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology.
|
http://arxiv.org/abs/1605.03661v3
|
http://arxiv.org/pdf/1605.03661v3.pdf
| null |
[
"Fredrik D. Johansson",
"Uri Shalit",
"David Sontag"
] |
[
"Causal Inference",
"counterfactual",
"Counterfactual Inference",
"Domain Adaptation",
"Representation Learning"
] | 2016-05-12T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "Causal inference is the process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect. The main difference between causal inference and inference of association is that the former analyzes the response of the effect variable when the cause is changed.",
"full_name": "Causal inference",
"introduced_year": 2000,
"main_collection": null,
"name": "Causal inference",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/optimization-landscape-and-expressivity-of
|
1710.10928
| null | null |
Optimization Landscape and Expressivity of Deep CNNs
|
We analyze the loss landscape and expressiveness of practical deep
convolutional neural networks (CNNs) with shared weights and max pooling
layers. We show that such CNNs produce linearly independent features at a
"wide" layer which has more neurons than the number of training samples. This
condition holds e.g. for the VGG network. Furthermore, we provide for such wide
CNNs necessary and sufficient conditions for global minima with zero training
error. For the case where the wide layer is followed by a fully connected layer
we show that almost every critical point of the empirical loss is a global
minimum with zero training error. Our analysis suggests that both depth and
width are very important in deep learning. While depth brings more
representational power and allows the network to learn high level features,
width smoothes the optimization landscape of the loss function in the sense
that a sufficiently wide network has a well-behaved loss surface with almost no
bad local minima.
| null |
http://arxiv.org/abs/1710.10928v2
|
http://arxiv.org/pdf/1710.10928v2.pdf
|
ICML 2018 7
|
[
"Quynh Nguyen",
"Matthias Hein"
] |
[] | 2017-10-30T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2011
|
http://proceedings.mlr.press/v80/nguyen18a/nguyen18a.pdf
|
optimization-landscape-and-expressivity-of-1
| 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": "",
"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": 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": "",
"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, Ethereum has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Ethereum transaction not confirmed, your Ethereum wallet not showing balance, or you're trying to recover a lost Ethereum wallet, knowing where to get help is essential. That’s why the Ethereum customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Ethereum Customer Support Number +1-833-534-1729\r\nEthereum operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Ethereum Transaction Not Confirmed\r\nOne of the most common concerns is when a Ethereum transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Ethereum Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Ethereum wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Ethereum Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Ethereum wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Ethereum Deposit Not Received\r\nIf someone has sent you Ethereum but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Ethereum deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Ethereum Transaction Stuck or Pending\r\nSometimes your Ethereum transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Ethereum Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Ethereum wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Ethereum Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Ethereum tech.\r\n\r\n24/7 Availability: Ethereum doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Ethereum Support and Wallet Issues\r\nQ1: Can Ethereum support help me recover stolen BTC?\r\nA: While Ethereum transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Ethereum transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Ethereum’s official number (Ethereum is decentralized), it connects you to trained professionals experienced in resolving all major Ethereum issues.\r\n\r\nFinal Thoughts\r\nEthereum is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Ethereum transaction not confirmed, your Ethereum wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Ethereum customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Ethereum Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "If you have questions or want to make special travel arrangements, you can make them online or call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. For hearing or speech impaired assistance dial 711 to be connected through the National Relay Service.",
"name": "Convolutional Neural Networks",
"parent": "Image Models"
},
"name": "Ethereum Customer Service Number +1-833-534-1729",
"source_title": "Very Deep Convolutional Networks for Large-Scale Image Recognition",
"source_url": "http://arxiv.org/abs/1409.1556v6"
}
] |
https://paperswithcode.com/paper/bayesian-quadrature-for-multiple-related
|
1801.04153
| null | null |
Bayesian Quadrature for Multiple Related Integrals
|
Bayesian probabilistic numerical methods are a set of tools providing
posterior distributions on the output of numerical methods. The use of these
methods is usually motivated by the fact that they can represent our
uncertainty due to incomplete/finite information about the continuous
mathematical problem being approximated. In this paper, we demonstrate that
this paradigm can provide additional advantages, such as the possibility of
transferring information between several numerical methods. This allows users
to represent uncertainty in a more faithful manner and, as a by-product,
provide increased numerical efficiency. We propose the first such numerical
method by extending the well-known Bayesian quadrature algorithm to the case
where we are interested in computing the integral of several related functions.
We then prove convergence rates for the method in the well-specified and
misspecified cases, and demonstrate its efficiency in the context of
multi-fidelity models for complex engineering systems and a problem of global
illumination in computer graphics.
| null |
http://arxiv.org/abs/1801.04153v7
|
http://arxiv.org/pdf/1801.04153v7.pdf
|
ICML 2018 7
|
[
"Xiaoyue Xi",
"François-Xavier Briol",
"Mark Girolami"
] |
[] | 2018-01-12T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=1890
|
http://proceedings.mlr.press/v80/xi18a/xi18a.pdf
|
bayesian-quadrature-for-multiple-related-1
| null |
[] |
https://paperswithcode.com/paper/neural-taylor-approximations-convergence-and
|
1611.02345
| null | null |
Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks
|
Modern convolutional networks, incorporating rectifiers and max-pooling, are
neither smooth nor convex; standard guarantees therefore do not apply.
Nevertheless, methods from convex optimization such as gradient descent and
Adam are widely used as building blocks for deep learning algorithms. This
paper provides the first convergence guarantee applicable to modern convnets,
which furthermore matches a lower bound for convex nonsmooth functions. The key
technical tool is the neural Taylor approximation -- a straightforward
application of Taylor expansions to neural networks -- and the associated
Taylor loss. Experiments on a range of optimizers, layers, and tasks provide
evidence that the analysis accurately captures the dynamics of neural
optimization. The second half of the paper applies the Taylor approximation to
isolate the main difficulty in training rectifier nets -- that gradients are
shattered -- and investigates the hypothesis that, by exploring the space of
activation configurations more thoroughly, adaptive optimizers such as RMSProp
and Adam are able to converge to better solutions.
| null |
http://arxiv.org/abs/1611.02345v3
|
http://arxiv.org/pdf/1611.02345v3.pdf
|
ICML 2017 8
|
[
"David Balduzzi",
"Brian McWilliams",
"Tony Butler-Yeoman"
] |
[] | 2016-11-07T00:00:00 |
https://icml.cc/Conferences/2017/Schedule?showEvent=600
|
http://proceedings.mlr.press/v70/balduzzi17c/balduzzi17c.pdf
|
neural-taylor-approximations-convergence-and-1
| 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"
}
] |
https://paperswithcode.com/paper/adversarial-auto-encoders-for-speech-based
|
1806.02146
| null | null |
Adversarial Auto-encoders for Speech Based Emotion Recognition
|
Recently, generative adversarial networks and adversarial autoencoders have
gained a lot of attention in machine learning community due to their
exceptional performance in tasks such as digit classification and face
recognition. They map the autoencoder's bottleneck layer output (termed as code
vectors) to different noise Probability Distribution Functions (PDFs), that can
be further regularized to cluster based on class information. In addition, they
also allow a generation of synthetic samples by sampling the code vectors from
the mapped PDFs. Inspired by these properties, we investigate the application
of adversarial autoencoders to the domain of emotion recognition. Specifically,
we conduct experiments on the following two aspects: (i) their ability to
encode high dimensional feature vector representations for emotional utterances
into a compressed space (with a minimal loss of emotion class discriminability
in the compressed space), and (ii) their ability to regenerate synthetic
samples in the original feature space, to be later used for purposes such as
training emotion recognition classifiers. We demonstrate the promise of
adversarial autoencoders with regards to these aspects on the Interactive
Emotional Dyadic Motion Capture (IEMOCAP) corpus and present our analysis.
| null |
http://arxiv.org/abs/1806.02146v1
|
http://arxiv.org/pdf/1806.02146v1.pdf
| null |
[
"Saurabh Sahu",
"Rahul Gupta",
"Ganesh Sivaraman",
"Wael Abd-Almageed",
"Carol Espy-Wilson"
] |
[
"Emotion Recognition",
"Face Recognition"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/strongly-typed-agents-are-guaranteed-to
|
1702.07450
| null | null |
Strongly-Typed Agents are Guaranteed to Interact Safely
|
As artificial agents proliferate, it is becoming increasingly important to
ensure that their interactions with one another are well-behaved. In this
paper, we formalize a common-sense notion of when algorithms are well-behaved:
an algorithm is safe if it does no harm. Motivated by recent progress in deep
learning, we focus on the specific case where agents update their actions
according to gradient descent. The paper shows that that gradient descent
converges to a Nash equilibrium in safe games. The main contribution is to
define strongly-typed agents and show they are guaranteed to interact safely,
thereby providing sufficient conditions to guarantee safe interactions. A
series of examples show that strong-typing generalizes certain key features of
convexity, is closely related to blind source separation, and introduces a new
perspective on classical multilinear games based on tensor decomposition.
| null |
http://arxiv.org/abs/1702.07450v2
|
http://arxiv.org/pdf/1702.07450v2.pdf
|
ICML 2017 8
|
[
"David Balduzzi"
] |
[
"blind source separation",
"Common Sense Reasoning",
"Tensor Decomposition"
] | 2017-02-24T00:00:00 |
https://icml.cc/Conferences/2017/Schedule?showEvent=599
|
http://proceedings.mlr.press/v70/balduzzi17a/balduzzi17a.pdf
|
strongly-typed-agents-are-guaranteed-to-1
| null |
[] |
https://paperswithcode.com/paper/multi-chart-generative-surface-modeling
|
1806.02143
| null | null |
Multi-chart Generative Surface Modeling
|
This paper introduces a 3D shape generative model based on deep neural
networks. A new image-like (i.e., tensor) data representation for genus-zero 3D
shapes is devised. It is based on the observation that complicated shapes can
be well represented by multiple parameterizations (charts), each focusing on a
different part of the shape. The new tensor data representation is used as
input to Generative Adversarial Networks for the task of 3D shape generation.
The 3D shape tensor representation is based on a multi-chart structure that
enjoys a shape covering property and scale-translation rigidity.
Scale-translation rigidity facilitates high quality 3D shape learning and
guarantees unique reconstruction. The multi-chart structure uses as input a
dataset of 3D shapes (with arbitrary connectivity) and a sparse correspondence
between them. The output of our algorithm is a generative model that learns the
shape distribution and is able to generate novel shapes, interpolate shapes,
and explore the generated shape space. The effectiveness of the method is
demonstrated for the task of anatomic shape generation including human body and
bone (teeth) shape generation.
|
The new tensor data representation is used as input to Generative Adversarial Networks for the task of 3D shape generation.
|
http://arxiv.org/abs/1806.02143v3
|
http://arxiv.org/pdf/1806.02143v3.pdf
| null |
[
"Heli Ben-Hamu",
"Haggai Maron",
"Itay Kezurer",
"Gal Avineri",
"Yaron Lipman"
] |
[
"3D Shape Generation",
"Translation"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-new-framework-for-machine-intelligence
|
1806.02137
| null | null |
A New Framework for Machine Intelligence: Concepts and Prototype
|
Machine learning (ML) and artificial intelligence (AI) have become hot topics
in many information processing areas, from chatbots to scientific data
analysis. At the same time, there is uncertainty about the possibility of
extending predominant ML technologies to become general solutions with
continuous learning capabilities. Here, a simple, yet comprehensive,
theoretical framework for intelligent systems is presented. A combination of
Mirror Compositional Representations (MCR) and a Solution-Critic Loop (SCL) is
proposed as a generic approach for different types of problems. A prototype
implementation is presented for document comparison using English Wikipedia
corpus.
| null |
http://arxiv.org/abs/1806.02137v1
|
http://arxiv.org/pdf/1806.02137v1.pdf
| null |
[
"Abel Torres Montoya"
] |
[
"BIG-bench Machine Learning"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/efficient-differentiable-programming-in-a
|
1806.02136
| null | null |
Efficient Differentiable Programming in a Functional Array-Processing Language
|
We present a system for the automatic differentiation of a higher-order
functional array-processing language. The core functional language underlying
this system simultaneously supports both source-to-source automatic
differentiation and global optimizations such as loop transformations. Thanks
to this feature, we demonstrate how for some real-world machine learning and
computer vision benchmarks, the system outperforms the state-of-the-art
automatic differentiation tools.
|
We present a system for the automatic differentiation of a higher-order functional array-processing language.
|
http://arxiv.org/abs/1806.02136v1
|
http://arxiv.org/pdf/1806.02136v1.pdf
| null |
[
"Amir Shaikhha",
"Andrew Fitzgibbon",
"Dimitrios Vytiniotis",
"Simon Peyton Jones",
"Christoph Koch"
] |
[
"BIG-bench Machine Learning"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/addendum-to-htn-acting-a-formalism-and-an
|
1806.02127
| null | null |
Addendum to "HTN Acting: A Formalism and an Algorithm"
|
Hierarchical Task Network (HTN) planning is a practical and efficient approach to planning when the 'standard operating procedures' for a domain are available. Like Belief-Desire-Intention (BDI) agent reasoning, HTN planning performs hierarchical and context-based refinement of goals into subgoals and basic actions. However, while HTN planners 'lookahead' over the consequences of choosing one refinement over another, BDI agents interleave refinement with acting. There has been renewed interest in making HTN planners behave more like BDI agent systems, e.g. to have a unified representation for acting and planning. However, past work on the subject has remained informal or implementation-focused. This paper is a formal account of 'HTN acting', which supports interleaved deliberation, acting, and failure recovery. We use the syntax of the most general HTN planning formalism and build on its core semantics, and we provide an algorithm which combines our new formalism with the processing of exogenous events. We also study the properties of HTN acting and its relation to HTN planning.
| null |
https://arxiv.org/abs/1806.02127v2
|
https://arxiv.org/pdf/1806.02127v2.pdf
| null |
[
"Lavindra de Silva"
] |
[] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/microscopy-cell-segmentation-via-adversarial
|
1709.05860
| null | null |
Microscopy Cell Segmentation via Adversarial Neural Networks
|
We present a novel method for cell segmentation in microscopy images which is
inspired by the Generative Adversarial Neural Network (GAN) approach. Our
framework is built on a pair of two competitive artificial neural networks,
with a unique architecture, termed Rib Cage, which are trained simultaneously
and together define a min-max game resulting in an accurate segmentation of a
given image. Our approach has two main strengths, similar to the GAN, the
method does not require a formulation of a loss function for the optimization
process. This allows training on a limited amount of annotated data in a weakly
supervised manner. Promising segmentation results on real fluorescent
microscopy data are presented. The code is freely available at:
https://github.com/arbellea/DeepCellSeg.git
|
We present a novel method for cell segmentation in microscopy images which is inspired by the Generative Adversarial Neural Network (GAN) approach.
|
http://arxiv.org/abs/1709.05860v4
|
http://arxiv.org/pdf/1709.05860v4.pdf
| null |
[
"Assaf Arbelle",
"Tammy Riklin Raviv"
] |
[
"Cell Segmentation",
"Segmentation"
] | 2017-09-18T00:00:00 | null | null | null | 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": "",
"description": "In today’s digital age, Dogecoin has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're trying to recover a lost Dogecoin wallet, knowing where to get help is essential. That’s why the Dogecoin customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Dogecoin Customer Support Number +1-833-534-1729\r\nDogecoin operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Dogecoin Transaction Not Confirmed\r\nOne of the most common concerns is when a Dogecoin transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Dogecoin Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Dogecoin wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Dogecoin Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Dogecoin wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Dogecoin Deposit Not Received\r\nIf someone has sent you Dogecoin but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Dogecoin deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Dogecoin Transaction Stuck or Pending\r\nSometimes your Dogecoin transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Dogecoin Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Dogecoin wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Dogecoin 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 Dogecoin tech.\r\n\r\n24/7 Availability: Dogecoin doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Dogecoin Support and Wallet Issues\r\nQ1: Can Dogecoin support help me recover stolen BTC?\r\nA: While Dogecoin transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Dogecoin transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Dogecoin’s official number (Dogecoin is decentralized), it connects you to trained professionals experienced in resolving all major Dogecoin issues.\r\n\r\nFinal Thoughts\r\nDogecoin is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Dogecoin customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Dogecoin Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Dogecoin Customer Service Number +1-833-534-1729",
"source_title": "Generative Adversarial Networks",
"source_url": "https://arxiv.org/abs/1406.2661v1"
}
] |
https://paperswithcode.com/paper/textray-mining-clinical-reports-to-gain-a
|
1806.02121
| null | null |
TextRay: Mining Clinical Reports to Gain a Broad Understanding of Chest X-rays
|
The chest X-ray (CXR) is by far the most commonly performed radiological
examination for screening and diagnosis of many cardiac and pulmonary diseases.
There is an immense world-wide shortage of physicians capable of providing
rapid and accurate interpretation of this study. A radiologist-driven analysis
of over two million CXR reports generated an ontology including the 40 most
prevalent pathologies on CXR. By manually tagging a relatively small set of
sentences, we were able to construct a training set of 959k studies. A deep
learning model was trained to predict the findings given the patient frontal
and lateral scans. For 12 of the findings we compare the model performance
against a team of radiologists and show that in most cases the radiologists
agree on average more with the algorithm than with each other.
| null |
http://arxiv.org/abs/1806.02121v1
|
http://arxiv.org/pdf/1806.02121v1.pdf
| null |
[
"Jonathan Laserson",
"Christine Dan Lantsman",
"Michal Cohen-Sfady",
"Itamar Tamir",
"Eli Goz",
"Chen Brestel",
"Shir Bar",
"Maya Atar",
"Eldad Elnekave"
] |
[] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/attention-incorporate-network-a-network-can
|
1806.03961
| null |
S1g_EsActm
|
Attention Incorporate Network: A network can adapt various data size
|
In traditional neural networks for image processing, the inputs of the neural
networks should be the same size such as 224*224*3. But how can we train the
neural net model with different input size? A common way to do is image
deformation which accompany a problem of information loss (e.g. image crop or
wrap). Sequence model(RNN, LSTM, etc.) can accept different size of input like
text and audio. But one disadvantage for sequence model is that the previous
information will become more fragmentary during the transfer in time step, it
will make the network hard to train especially for long sequential data. In
this paper we propose a new network structure called Attention Incorporate
Network(AIN). It solve the problem of different size of inputs including:
images, text, audio, and extract the key features of the inputs by attention
mechanism, pay different attention depends on the importance of the features
not rely on the data size. Experimentally, AIN achieve a higher accuracy,
better convergence comparing to the same size of other network structure
| null |
http://arxiv.org/abs/1806.03961v1
|
http://arxiv.org/pdf/1806.03961v1.pdf
|
ICLR 2019 5
|
[
"Liangbo He",
"Hao Sun"
] |
[] | 2018-06-06T00:00:00 |
https://openreview.net/forum?id=S1g_EsActm
|
https://openreview.net/pdf?id=S1g_EsActm
|
attention-incorporate-network-a-network-can-1
| 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/bounding-bloat-in-genetic-programming
|
1806.02112
| null | null |
Bounding Bloat in Genetic Programming
|
While many optimization problems work with a fixed number of decision
variables and thus a fixed-length representation of possible solutions, genetic
programming (GP) works on variable-length representations. A naturally
occurring problem is that of bloat (unnecessary growth of solutions) slowing
down optimization. Theoretical analyses could so far not bound bloat and
required explicit assumptions on the magnitude of bloat. In this paper we
analyze bloat in mutation-based genetic programming for the two test functions
ORDER and MAJORITY. We overcome previous assumptions on the magnitude of bloat
and give matching or close-to-matching upper and lower bounds for the expected
optimization time. In particular, we show that the (1+1) GP takes (i)
$\Theta(T_{init} + n \log n)$ iterations with bloat control on ORDER as well as
MAJORITY; and (ii) $O(T_{init} \log T_{init} + n (\log n)^3)$ and
$\Omega(T_{init} + n \log n)$ (and $\Omega(T_{init} \log T_{init})$ for $n=1$)
iterations without bloat control on MAJORITY.
| null |
http://arxiv.org/abs/1806.02112v1
|
http://arxiv.org/pdf/1806.02112v1.pdf
| null |
[
"Benjamin Doerr",
"Timo Kötzing",
"J. A. Gregor Lagodzinski",
"Johannes Lengler"
] |
[] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/the-shattered-gradients-problem-if-resnets
|
1702.08591
| null | null |
The Shattered Gradients Problem: If resnets are the answer, then what is the question?
|
A long-standing obstacle to progress in deep learning is the problem of
vanishing and exploding gradients. Although, the problem has largely been
overcome via carefully constructed initializations and batch normalization,
architectures incorporating skip-connections such as highway and resnets
perform much better than standard feedforward architectures despite well-chosen
initialization and batch normalization. In this paper, we identify the
shattered gradients problem. Specifically, we show that the correlation between
gradients in standard feedforward networks decays exponentially with depth
resulting in gradients that resemble white noise whereas, in contrast, the
gradients in architectures with skip-connections are far more resistant to
shattering, decaying sublinearly. Detailed empirical evidence is presented in
support of the analysis, on both fully-connected networks and convnets.
Finally, we present a new "looks linear" (LL) initialization that prevents
shattering, with preliminary experiments showing the new initialization allows
to train very deep networks without the addition of skip-connections.
|
A long-standing obstacle to progress in deep learning is the problem of vanishing and exploding gradients.
|
http://arxiv.org/abs/1702.08591v2
|
http://arxiv.org/pdf/1702.08591v2.pdf
|
ICML 2017 8
|
[
"David Balduzzi",
"Marcus Frean",
"Lennox Leary",
"JP Lewis",
"Kurt Wan-Duo Ma",
"Brian McWilliams"
] |
[] | 2017-02-28T00:00:00 |
https://icml.cc/Conferences/2017/Schedule?showEvent=601
|
http://proceedings.mlr.press/v70/balduzzi17b/balduzzi17b.pdf
|
the-shattered-gradients-problem-if-resnets-1
| null |
[] |
https://paperswithcode.com/paper/conditionally-independent-multiresolution
|
1802.09086
| null | null |
Conditionally Independent Multiresolution Gaussian Processes
|
The multiresolution Gaussian process (GP) has gained increasing attention as
a viable approach towards improving the quality of approximations in GPs that
scale well to large-scale data. Most of the current constructions assume full
independence across resolutions. This assumption simplifies the inference, but
it underestimates the uncertainties in transitioning from one resolution to
another. This in turn results in models which are prone to overfitting in the
sense of excessive sensitivity to the chosen resolution, and predictions which
are non-smooth at the boundaries. Our contribution is a new construction which
instead assumes conditional independence among GPs across resolutions. We show
that relaxing the full independence assumption enables robustness against
overfitting, and that it delivers predictions that are smooth at the
boundaries. Our new model is compared against current state of the art on 2
synthetic and 9 real-world datasets. In most cases, our new conditionally
independent construction performed favorably when compared against models based
on the full independence assumption. In particular, it exhibits little to no
signs of overfitting.
|
This in turn results in models which are prone to overfitting in the sense of excessive sensitivity to the chosen resolution, and predictions which are non-smooth at the boundaries.
|
http://arxiv.org/abs/1802.09086v3
|
http://arxiv.org/pdf/1802.09086v3.pdf
| null |
[
"Jalil Taghia",
"Thomas B. Schön"
] |
[
"Bayesian Inference",
"Gaussian Processes"
] | 2018-02-25T00:00:00 | 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/properties-of-interaction-networks-structure
|
1805.11359
| null | null |
Properties of interaction networks, structure coefficients, and benefit-to-cost ratios
|
In structured populations the spatial arrangement of cooperators and
defectors on the interaction graph together with the structure of the graph
itself determines the game dynamics and particularly whether or not fixation of
cooperation (or defection) is favored. For a single cooperator (and a single
defector) and a network described by a regular graph the question of fixation
can be addressed by a single parameter, the structure coefficient. As this
quantity is generic for any regular graph, we may call it the generic structure
coefficient. For two and more cooperators (or several defectors) fixation
properties can also be assigned by structure coefficients. These structure
coefficients, however, depend on the arrangement of cooperators and defectors
which we may interpret as a configuration of the game. Moreover, the
coefficients are specific for a given interaction network modeled as regular
graph, which is why we may call them specific structure coefficients. In this
paper, we study how specific structure coefficients vary over interaction
graphs and link the distributions obtained over different graphs to spectral
properties of interaction networks. We also discuss implications for the
benefit-to-cost ratios of donation games.
| null |
http://arxiv.org/abs/1805.11359v2
|
http://arxiv.org/pdf/1805.11359v2.pdf
| null |
[
"Hendrik Richter"
] |
[] | 2018-05-29T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/convolutional-sequence-to-sequence-non
|
1806.02078
| null | null |
Convolutional Sequence to Sequence Non-intrusive Load Monitoring
|
A convolutional sequence to sequence non-intrusive load monitoring model is
proposed in this paper. Gated linear unit convolutional layers are used to
extract information from the sequences of aggregate electricity consumption.
Residual blocks are also introduced to refine the output of the neural network.
The partially overlapped output sequences of the network are averaged to
produce the final output of the model. We apply the proposed model to the REDD
dataset and compare it with the convolutional sequence to point model in the
literature. Results show that the proposed model is able to give satisfactory
disaggregation performance for appliances with varied characteristics.
| null |
http://arxiv.org/abs/1806.02078v1
|
http://arxiv.org/pdf/1806.02078v1.pdf
| null |
[
"Kunjin Chen",
"Qin Wang",
"Ziyu He",
"Kunlong Chen",
"Jun Hu",
"Jinliang He"
] |
[
"Non-Intrusive Load Monitoring"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-exploration-via-randomized-value
|
1703.07608
| null | null |
Deep Exploration via Randomized Value Functions
|
We study the use of randomized value functions to guide deep exploration in reinforcement learning. This offers an elegant means for synthesizing statistically and computationally efficient exploration with common practical approaches to value function learning. We present several reinforcement learning algorithms that leverage randomized value functions and demonstrate their efficacy through computational studies. We also prove a regret bound that establishes statistical efficiency with a tabular representation.
| null |
https://arxiv.org/abs/1703.07608v5
|
https://arxiv.org/pdf/1703.07608v5.pdf
| null |
[
"Ian Osband",
"Benjamin Van Roy",
"Daniel Russo",
"Zheng Wen"
] |
[
"Efficient Exploration",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2017-03-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-fluids-a-generative-network-for
|
1806.02071
| null | null |
Deep Fluids: A Generative Network for Parameterized Fluid Simulations
|
This paper presents a novel generative model to synthesize fluid simulations
from a set of reduced parameters. A convolutional neural network is trained on
a collection of discrete, parameterizable fluid simulation velocity fields. Due
to the capability of deep learning architectures to learn representative
features of the data, our generative model is able to accurately approximate
the training data set, while providing plausible interpolated in-betweens. The
proposed generative model is optimized for fluids by a novel loss function that
guarantees divergence-free velocity fields at all times. In addition, we
demonstrate that we can handle complex parameterizations in reduced spaces, and
advance simulations in time by integrating in the latent space with a second
network. Our method models a wide variety of fluid behaviors, thus enabling
applications such as fast construction of simulations, interpolation of fluids
with different parameters, time re-sampling, latent space simulations, and
compression of fluid simulation data. Reconstructed velocity fields are
generated up to 700x faster than re-simulating the data with the underlying CPU
solver, while achieving compression rates of up to 1300x.
|
This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters.
|
http://arxiv.org/abs/1806.02071v2
|
http://arxiv.org/pdf/1806.02071v2.pdf
| null |
[
"Byung-soo Kim",
"Vinicius C. Azevedo",
"Nils Thuerey",
"Theodore Kim",
"Markus Gross",
"Barbara Solenthaler"
] |
[
"CPU"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/predicting-the-drivers-focus-of-attention-the
|
1705.03854
| null | null |
Predicting the Driver's Focus of Attention: the DR(eye)VE Project
|
In this work we aim to predict the driver's focus of attention. The goal is
to estimate what a person would pay attention to while driving, and which part
of the scene around the vehicle is more critical for the task. To this end we
propose a new computer vision model based on a multi-branch deep architecture
that integrates three sources of information: raw video, motion and scene
semantics. We also introduce DR(eye)VE, the largest dataset of driving scenes
for which eye-tracking annotations are available. This dataset features more
than 500,000 registered frames, matching ego-centric views (from glasses worn
by drivers) and car-centric views (from roof-mounted camera), further enriched
by other sensors measurements. Results highlight that several attention
patterns are shared across drivers and can be reproduced to some extent. The
indication of which elements in the scene are likely to capture the driver's
attention may benefit several applications in the context of human-vehicle
interaction and driver attention analysis.
|
In this work we aim to predict the driver's focus of attention.
|
http://arxiv.org/abs/1705.03854v3
|
http://arxiv.org/pdf/1705.03854v3.pdf
| null |
[
"Andrea Palazzi",
"Davide Abati",
"Simone Calderara",
"Francesco Solera",
"Rita Cucchiara"
] |
[] | 2017-05-10T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/pieapp-perceptual-image-error-assessment
|
1806.02067
| null | null |
PieAPP: Perceptual Image-Error Assessment through Pairwise Preference
|
The ability to estimate the perceptual error between images is an important
problem in computer vision with many applications. Although it has been studied
extensively, however, no method currently exists that can robustly predict
visual differences like humans. Some previous approaches used hand-coded
models, but they fail to model the complexity of the human visual system.
Others used machine learning to train models on human-labeled datasets, but
creating large, high-quality datasets is difficult because people are unable to
assign consistent error labels to distorted images. In this paper, we present a
new learning-based method that is the first to predict perceptual image error
like human observers. Since it is much easier for people to compare two given
images and identify the one more similar to a reference than to assign quality
scores to each, we propose a new, large-scale dataset labeled with the
probability that humans will prefer one image over another. We then train a
deep-learning model using a novel, pairwise-learning framework to predict the
preference of one distorted image over the other. Our key observation is that
our trained network can then be used separately with only one distorted image
and a reference to predict its perceptual error, without ever being trained on
explicit human perceptual-error labels. The perceptual error estimated by our
new metric, PieAPP, is well-correlated with human opinion. Furthermore, it
significantly outperforms existing algorithms, beating the state-of-the-art by
almost 3x on our test set in terms of binary error rate, while also
generalizing to new kinds of distortions, unlike previous learning-based
methods.
|
Our key observation is that our trained network can then be used separately with only one distorted image and a reference to predict its perceptual error, without ever being trained on explicit human perceptual-error labels.
|
http://arxiv.org/abs/1806.02067v1
|
http://arxiv.org/pdf/1806.02067v1.pdf
|
CVPR 2018 6
|
[
"Ekta Prashnani",
"Hong Cai",
"Yasamin Mostofi",
"Pradeep Sen"
] |
[
"Video Quality Assessment"
] | 2018-06-06T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Prashnani_PieAPP_Perceptual_Image-Error_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Prashnani_PieAPP_Perceptual_Image-Error_CVPR_2018_paper.pdf
|
pieapp-perceptual-image-error-assessment-1
| null |
[] |
https://paperswithcode.com/paper/explicit-inductive-bias-for-transfer-learning
|
1802.01483
| null | null |
Explicit Inductive Bias for Transfer Learning with Convolutional Networks
|
In inductive transfer learning, fine-tuning pre-trained convolutional
networks substantially outperforms training from scratch. When using
fine-tuning, the underlying assumption is that the pre-trained model extracts
generic features, which are at least partially relevant for solving the target
task, but would be difficult to extract from the limited amount of data
available on the target task. However, besides the initialization with the
pre-trained model and the early stopping, there is no mechanism in fine-tuning
for retaining the features learned on the source task. In this paper, we
investigate several regularization schemes that explicitly promote the
similarity of the final solution with the initial model. We show the benefit of
having an explicit inductive bias towards the initial model, and we eventually
recommend a simple $L^2$ penalty with the pre-trained model being a reference
as the baseline of penalty for transfer learning tasks.
|
In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch.
|
http://arxiv.org/abs/1802.01483v2
|
http://arxiv.org/pdf/1802.01483v2.pdf
|
ICML 2018 7
|
[
"Xuhong Li",
"Yves GRANDVALET",
"Franck DAVOINE"
] |
[
"Inductive Bias",
"Transfer Learning"
] | 2018-02-05T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2225
|
http://proceedings.mlr.press/v80/li18a/li18a.pdf
|
explicit-inductive-bias-for-transfer-learning-1
| null |
[] |
https://paperswithcode.com/paper/training-generative-reversible-networks
|
1806.01610
| null | null |
Training Generative Reversible Networks
|
Generative models with an encoding component such as autoencoders currently
receive great interest. However, training of autoencoders is typically
complicated by the need to train a separate encoder and decoder model that have
to be enforced to be reciprocal to each other. To overcome this problem,
by-design reversible neural networks (RevNets) had been previously used as
generative models either directly optimizing the likelihood of the data under
the model or using an adversarial approach on the generated data. Here, we
instead investigate their performance using an adversary on the latent space in
the adversarial autoencoder framework. We investigate the generative
performance of RevNets on the CelebA dataset, showing that generative RevNets
can generate coherent faces with similar quality as Variational Autoencoders.
This first attempt to use RevNets inside the adversarial autoencoder framework
slightly underperformed relative to recent advanced generative models using an
autoencoder component on CelebA, but this gap may diminish with further
optimization of the training setup of generative RevNets. In addition to the
experiments on CelebA, we show a proof-of-principle experiment on the MNIST
dataset suggesting that adversary-free trained RevNets can discover meaningful
latent dimensions without pre-specifying the number of dimensions of the latent
sampling distribution. In summary, this study shows that RevNets can be
employed in different generative training settings.
Source code for this study is at
https://github.com/robintibor/generative-reversible
|
This first attempt to use RevNets inside the adversarial autoencoder framework slightly underperformed relative to recent advanced generative models using an autoencoder component on CelebA, but this gap may diminish with further optimization of the training setup of generative RevNets.
|
http://arxiv.org/abs/1806.01610v4
|
http://arxiv.org/pdf/1806.01610v4.pdf
| null |
[
"Robin Tibor Schirrmeister",
"Patryk Chrabąszcz",
"Frank Hutter",
"Tonio Ball"
] |
[
"Decoder"
] | 2018-06-05T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "In today’s digital age, Solana 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 Solana transaction not confirmed, your Solana wallet not showing balance, or you're trying to recover a lost Solana wallet, knowing where to get help is essential. That’s why the Solana 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 Solana Customer Support Number +1-833-534-1729\r\nSolana 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. 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Knowing how to recover a lost Solana wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Solana Deposit Not Received\r\nIf someone has sent you Solana but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Solana deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Solana Transaction Stuck or Pending\r\nSometimes your Solana transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Solana Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Solana wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Solana 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 Solana tech.\r\n\r\n24/7 Availability: Solana 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 Solana Support and Wallet Issues\r\nQ1: Can Solana support help me recover stolen BTC?\r\nA: While Solana 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: Solana 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 Solana’s official number (Solana is decentralized), it connects you to trained professionals experienced in resolving all major Solana issues.\r\n\r\nFinal Thoughts\r\nSolana 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 Solana transaction not confirmed, your Solana wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Solana 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": "Solana 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": "Solana Customer Service Number +1-833-534-1729",
"source_title": "Reducing the Dimensionality of Data with Neural Networks",
"source_url": "https://science.sciencemag.org/content/313/5786/504"
}
] |
https://paperswithcode.com/paper/spatial-frequency-loss-for-learning
|
1806.02336
| null | null |
Spatial Frequency Loss for Learning Convolutional Autoencoders
|
This paper presents a learning method for convolutional autoencoders (CAEs)
for extracting features from images. CAEs can be obtained by utilizing
convolutional neural networks to learn an approximation to the identity
function in an unsupervised manner. The loss function based on the pixel loss
(PL) that is the mean squared error between the pixel values of original and
reconstructed images is the common choice for learning. However, using the loss
function leads to blurred reconstructed images. A method for learning CAEs
using a loss function computed from features reflecting spatial frequencies is
proposed to mitigate the problem. The blurs in reconstructed images show lack
of high spatial frequency components mainly constituting edges and detailed
textures that are important features for tasks such as object detection and
spatial matching. In order to evaluate the lack of components, a convolutional
layer with a Laplacian filter bank as weights is added to CAEs and the mean
squared error of features in a subband, called the spatial frequency loss
(SFL), is computed from the outputs of each filter. The learning is performed
using a loss function based on the SFL. Empirical evaluation demonstrates that
using the SFL reduces the blurs in reconstructed images.
|
In order to evaluate the lack of components, a convolutional layer with a Laplacian filter bank as weights is added to CAEs and the mean squared error of features in a subband, called the spatial frequency loss (SFL), is computed from the outputs of each filter.
|
http://arxiv.org/abs/1806.02336v1
|
http://arxiv.org/pdf/1806.02336v1.pdf
| null |
[
"Naoyuki Ichimura"
] |
[
"object-detection",
"Object Detection"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-hierarchical-item-categories-from
|
1806.02056
| null | null |
Learning Hierarchical Item Categories from Implicit Feedback Data for Efficient Recommendations and Browsing
|
Searching, browsing, and recommendations are common ways in which the "choice overload" faced by users in the online marketplace can be mitigated. In this paper we propose the use of hierarchical item categories, obtained from implicit feedback data, to enable efficient browsing and recommendations. We present a method of creating hierarchical item categories from implicit feedback data only i.e., without any other information on the items like name, genre etc. Categories created in this fashion are based on users' co-consumption of items. Thus, they can be more useful for users in finding interesting and relevant items while they are browsing through the hierarchy. We also show that this item hierarchy can be useful in making category based recommendations, which makes the recommendations more explainable and increases the diversity of the recommendations without compromising much on the accuracy. Item hierarchy can also be useful in the creation of an automatic item taxonomy skeleton by bypassing manual labeling and annotation. This can especially be useful for small vendors. Our data-driven hierarchical categories are based on hierarchical latent tree analysis (HLTA) which has been previously used for text analysis. We present a scaled up learning algorithm \emph{HLTA-Forest} so that HLTA can be applied to implicit feedback data.
|
Categories created in this fashion are based on users' co-consumption of items.
|
https://arxiv.org/abs/1806.02056v2
|
https://arxiv.org/pdf/1806.02056v2.pdf
| null |
[
"Farhan Khawar",
"Nevin L. Zhang"
] |
[
"Diversity"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/is-the-winner-really-the-best-a-critical
|
1806.02051
| null | null |
Why rankings of biomedical image analysis competitions should be interpreted with care
|
International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future.
| null |
https://arxiv.org/abs/1806.02051v2
|
https://arxiv.org/pdf/1806.02051v2.pdf
| null |
[
"Lena Maier-Hein",
"Matthias Eisenmann",
"Annika Reinke",
"Sinan Onogur",
"Marko Stankovic",
"Patrick Scholz",
"Tal Arbel",
"Hrvoje Bogunovic",
"Andrew P. Bradley",
"Aaron Carass",
"Carolin Feldmann",
"Alejandro F. Frangi",
"Peter M. Full",
"Bram van Ginneken",
"Allan Hanbury",
"Katrin Honauer",
"Michal Kozubek",
"Bennett A. Landman",
"Keno März",
"Oskar Maier",
"Klaus Maier-Hein",
"Bjoern H. Menze",
"Henning Müller",
"Peter F. Neher",
"Wiro Niessen",
"Nasir Rajpoot",
"Gregory C. Sharp",
"Korsuk Sirinukunwattana",
"Stefanie Speidel",
"Christian Stock",
"Danail Stoyanov",
"Abdel Aziz Taha",
"Fons van der Sommen",
"Ching-Wei Wang",
"Marc-André Weber",
"Guoyan Zheng",
"Pierre Jannin",
"Annette Kopp-Schneider"
] |
[] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/implicit-regularization-and-solution
|
1806.02046
| null | null |
Implicit regularization and solution uniqueness in over-parameterized matrix sensing
|
We consider whether algorithmic choices in over-parameterized linear matrix factorization introduce implicit regularization. We focus on noiseless matrix sensing over rank-$r$ positive semi-definite (PSD) matrices in $\mathbb{R}^{n \times n}$, with a sensing mechanism that satisfies restricted isometry properties (RIP). The algorithm we study is \emph{factored gradient descent}, where we model the low-rankness and PSD constraints with the factorization $UU^\top$, for $U \in \mathbb{R}^{n \times r}$. Surprisingly, recent work argues that the choice of $r \leq n$ is not pivotal: even setting $U \in \mathbb{R}^{n \times n}$ is sufficient for factored gradient descent to find the rank-$r$ solution, which suggests that operating over the factors leads to an implicit regularization. In this contribution, we provide a different perspective to the problem of implicit regularization. We show that under certain conditions, the PSD constraint by itself is sufficient to lead to a unique rank-$r$ matrix recovery, without implicit or explicit low-rank regularization. \emph{I.e.}, under assumptions, the set of PSD matrices, that are consistent with the observed data, is a singleton, regardless of the algorithm used.
| null |
https://arxiv.org/abs/1806.02046v2
|
https://arxiv.org/pdf/1806.02046v2.pdf
| null |
[
"Kelly Geyer",
"Anastasios Kyrillidis",
"Amir Kalev"
] |
[] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/medical-concept-embedding-with-time-aware
|
1806.02873
| null | null |
Medical Concept Embedding with Time-Aware Attention
|
Embeddings of medical concepts such as medication, procedure and diagnosis
codes in Electronic Medical Records (EMRs) are central to healthcare analytics.
Previous work on medical concept embedding takes medical concepts and EMRs as
words and documents respectively. Nevertheless, such models miss out the
temporal nature of EMR data. On the one hand, two consecutive medical concepts
do not indicate they are temporally close, but the correlations between them
can be revealed by the time gap. On the other hand, the temporal scopes of
medical concepts often vary greatly (e.g., \textit{common cold} and
\textit{diabetes}). In this paper, we propose to incorporate the temporal
information to embed medical codes. Based on the Continuous Bag-of-Words model,
we employ the attention mechanism to learn a "soft" time-aware context window
for each medical concept. Experiments on public and proprietary datasets
through clustering and nearest neighbour search tasks demonstrate the
effectiveness of our model, showing that it outperforms five state-of-the-art
baselines.
|
Embeddings of medical concepts such as medication, procedure and diagnosis codes in Electronic Medical Records (EMRs) are central to healthcare analytics.
|
http://arxiv.org/abs/1806.02873v1
|
http://arxiv.org/pdf/1806.02873v1.pdf
| null |
[
"Xiangrui Cai",
"Jinyang Gao",
"Kee Yuan Ngiam",
"Beng Chin Ooi",
"Ying Zhang",
"Xiaojie Yuan"
] |
[
"Clustering"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/simplex-search-based-brain-storm-optimization
|
1712.03166
| null | null |
Simplex Search Based Brain Storm Optimization
|
Through modeling human's brainstorming process, the brain storm optimization
(BSO) algorithm has become a promising population-based evolutionary algorithm.
However, BSO is pointed out that it possesses a degenerated L-curve phenomenon,
i.e., it often gets near optimum quickly but needs much more cost to improve
the accuracy. To overcome this question in this paper, an excellent direct
search based local solver, the Nelder-Mead Simplex (NMS) method is adopted in
BSO. Through combining BSO's exploration ability and NMS's exploitation ability
together, a simplex search based BSO (Simplex-BSO) is developed via a better
balance between global exploration and local exploitation. Simplex-BSO is shown
to be able to eliminate the degenerated L-curve phenomenon on unimodal
functions, and alleviate significantly this phenomenon on multimodal functions.
Large number of experimental results show that Simplex-BSO is a promising
algorithm for global optimization problems.
| null |
http://arxiv.org/abs/1712.03166v3
|
http://arxiv.org/pdf/1712.03166v3.pdf
| null |
[
"Wei Chen",
"YingYing Cao",
"Shi Cheng",
"Yifei Sun",
"Qunfeng Liu",
"Yun Li"
] |
[
"global-optimization"
] | 2017-10-24T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/degrees-of-freedom-and-model-selection-for
|
1806.02034
| null | null |
Degrees of Freedom and Model Selection for k-means Clustering
|
This paper investigates the model degrees of freedom in k-means clustering. An extension of Stein's lemma provides an expression for the effective degrees of freedom in the k-means model. Approximating the degrees of freedom in practice requires simplifications of this expression, however empirical studies evince the appropriateness of our proposed approach. The practical relevance of this new degrees of freedom formulation for k-means is demonstrated through model selection using the Bayesian Information Criterion. The reliability of this method is validated through experiments on simulated data as well as on a large collection of publicly available benchmark data sets from diverse application areas. Comparisons with popular existing techniques indicate that this approach is extremely competitive for selecting high quality clustering solutions. Code to implement the proposed approach is available in the form of an R package from https://github.com/DavidHofmeyr/edfkmeans.
|
This paper investigates the model degrees of freedom in k-means clustering.
|
https://arxiv.org/abs/1806.02034v4
|
https://arxiv.org/pdf/1806.02034v4.pdf
| null |
[
"David P. Hofmeyr"
] |
[
"Clustering",
"LEMMA",
"Model Selection"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/killing-four-birds-with-one-gaussian-process
|
1806.02032
| null | null |
Killing four birds with one Gaussian process: the relation between different test-time attacks
|
In machine learning (ML) security, attacks like evasion, model stealing or membership inference are generally studied in individually. Previous work has also shown a relationship between some attacks and decision function curvature of the targeted model. Consequently, we study an ML model allowing direct control over the decision surface curvature: Gaussian Process classifiers (GPCs). For evasion, we find that changing GPC's curvature to be robust against one attack algorithm boils down to enabling a different norm or attack algorithm to succeed. This is backed up by our formal analysis showing that static security guarantees are opposed to learning. Concerning intellectual property, we show formally that lazy learning does not necessarily leak all information when applied. In practice, often a seemingly secure curvature can be found. For example, we are able to secure GPC against empirical membership inference by proper configuration. In this configuration, however, the GPC's hyper-parameters are leaked, e.g. model reverse engineering succeeds. We conclude that attacks on classification should not be studied in isolation, but in relation to each other.
| null |
https://arxiv.org/abs/1806.02032v3
|
https://arxiv.org/pdf/1806.02032v3.pdf
| null |
[
"Kathrin Grosse",
"Michael T. Smith",
"Michael Backes"
] |
[
"General Classification",
"Relation"
] | 2018-06-06T00:00:00 | 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/real-time-surgical-tools-recognition-in-total
|
1806.02031
| null | null |
Real-time Surgical Tools Recognition in Total Knee Arthroplasty Using Deep Neural Networks
|
Total knee arthroplasty (TKA) is a commonly performed surgical procedure to
mitigate knee pain and improve functions for people with knee arthritis. The
procedure is complicated due to the different surgical tools used in the stages
of surgery. The recognition of surgical tools in real-time can be a solution to
simplify surgical procedures for the surgeon. Also, the presence and movement
of tools in surgery are crucial information for the recognition of the
operational phase and to identify the surgical workflow. Therefore, this
research proposes the development of a real-time system for the recognition of
surgical tools during surgery using a convolutional neural network (CNN).
Surgeons wearing smart glasses can see essential information about tools during
surgery that may reduce the complication of the procedures. To evaluate the
performance of the proposed method, we calculated and compared the Mean Average
Precision (MAP) with state-of-the-art methods which are fast R-CNN and
deformable part models (DPM). We achieved 87.6% mAP which is better in
comparison to the existing methods. With the additional improvements of our
proposed method, it can be a future point of reference, also the baseline for
operational phase recognition.
| null |
http://arxiv.org/abs/1806.02031v1
|
http://arxiv.org/pdf/1806.02031v1.pdf
| null |
[
"Moazzem Hossain",
"Soichi Nishio",
"Takafumi Hiranaka",
"Syoji Kobashi"
] |
[] | 2018-06-06T00:00:00 | null | null | null | 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": "",
"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/vision/blob/5e9ebe8dadc0ea2841a46cfcd82a93b4ce0d4519/torchvision/ops/roi_pool.py#L10",
"description": "**Region of Interest Pooling**, or **RoIPool**, is an operation for extracting a small feature map (e.g., $7×7$) from each RoI in detection and segmentation based tasks. Features are extracted from each candidate box, and thereafter in models like [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn), are then classified and bounding box regression performed.\r\n\r\nThe actual scaling to, e.g., $7×7$, occurs by dividing the region proposal into equally sized sections, finding the largest value in each section, and then copying these max values to the output buffer. In essence, **RoIPool** is [max pooling](https://paperswithcode.com/method/max-pooling) on a discrete grid based on a box.\r\n\r\nImage Source: [Joyce Xu](https://towardsdatascience.com/deep-learning-for-object-detection-a-comprehensive-review-73930816d8d9)",
"full_name": "RoIPool",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**RoI Feature Extractors** are used to extract regions of interest features for tasks such as object detection. Below you can find a continuously updating list of RoI Feature Extractors.",
"name": "RoI Feature Extractors",
"parent": null
},
"name": "RoIPool",
"source_title": "Rich feature hierarchies for accurate object detection and semantic segmentation",
"source_url": "http://arxiv.org/abs/1311.2524v5"
},
{
"code_snippet_url": "",
"description": "**Fast R-CNN** is an object detection model that improves in its predecessor [R-CNN](https://paperswithcode.com/method/r-cnn) in a number of ways. Instead of extracting CNN features independently for each region of interest, Fast R-CNN aggregates them into a single forward pass over the image; i.e. regions of interest from the same image share computation and memory in the forward and backward passes.",
"full_name": "Fast R-CNN",
"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": "Fast R-CNN",
"source_title": "Fast R-CNN",
"source_url": "http://arxiv.org/abs/1504.08083v2"
}
] |
https://paperswithcode.com/paper/screenernet-learning-self-paced-curriculum
|
1801.00904
| null | null |
ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks
|
We propose to learn a curriculum or a syllabus for supervised learning and
deep reinforcement learning with deep neural networks by an attachable deep
neural network, called ScreenerNet. Specifically, we learn a weight for each
sample by jointly training the ScreenerNet and the main network in an
end-to-end self-paced fashion. The ScreenerNet neither has sampling bias nor
requires to remember the past learning history. We show the networks augmented
with the ScreenerNet achieve early convergence with better accuracy than the
state-of-the-art curricular learning methods in extensive experiments using
three popular vision datasets such as MNIST, CIFAR10 and Pascal VOC2012, and a
Cart-pole task using Deep Q-learning. Moreover, the ScreenerNet can extend
other curriculum learning methods such as Prioritized Experience Replay (PER)
for further accuracy improvement.
| null |
http://arxiv.org/abs/1801.00904v4
|
http://arxiv.org/pdf/1801.00904v4.pdf
| null |
[
"Tae-hoon Kim",
"Jonghyun Choi"
] |
[
"Deep Reinforcement Learning",
"Q-Learning",
"Reinforcement Learning"
] | 2018-01-03T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "**Prioritized Experience Replay** is a type of [experience replay](https://paperswithcode.com/method/experience-replay) in reinforcement learning where we more frequently replay transitions with high expected learning progress, as measured by the magnitude of their temporal-difference (TD) error. This prioritization can lead to a loss of diversity, which is alleviated with stochastic prioritization, and introduce bias, which can be corrected with importance sampling.\r\n\r\nThe stochastic sampling method interpolates between pure greedy prioritization and uniform random sampling. The probability of being sampled is ensured to be monotonic in a transition's priority, while guaranteeing a non-zero probability even for the lowest-priority transition. Concretely, define the probability of sampling transition $i$ as\r\n\r\n$$P(i) = \\frac{p_i^{\\alpha}}{\\sum_k p_k^{\\alpha}}$$\r\n\r\nwhere $p_i > 0$ is the priority of transition $i$. The exponent $\\alpha$ determines how much prioritization is used, with $\\alpha=0$ corresponding to the uniform case.\r\n\r\nPrioritized replay introduces bias because it changes this distribution in an uncontrolled fashion, and therefore changes the solution that the estimates will converge to. We can correct this bias by using\r\nimportance-sampling (IS) weights:\r\n\r\n$$ w\\_{i} = \\left(\\frac{1}{N}\\cdot\\frac{1}{P\\left(i\\right)}\\right)^{\\beta} $$\r\n\r\nthat fully compensates for the non-uniform probabilities $P\\left(i\\right)$ if $\\beta = 1$. These weights can be folded into the [Q-learning](https://paperswithcode.com/method/q-learning) update by using $w\\_{i}\\delta\\_{i}$ instead of $\\delta\\_{i}$ - weighted IS rather than ordinary IS. For stability reasons, we always normalize weights by $1/\\max\\_{i}w\\_{i}$ so\r\nthat they only scale the update downwards.\r\n\r\nThe two types of prioritization are proportional based, where $p\\_{i} = |\\delta\\_{i}| + \\epsilon$ and rank-based, where $p\\_{i} = \\frac{1}{\\text{rank}\\left(i\\right)}$, the latter where $\\text{rank}\\left(i\\right)$ is the rank of transition $i$ when the replay memory is sorted according to |$\\delta\\_{i}$|, For proportional based, hyperparameters used were $\\alpha = 0.7$, $\\beta\\_{0} = 0.5$. For the rank-based variant, hyperparameters used were $\\alpha = 0.6$, $\\beta\\_{0} = 0.4$.",
"full_name": "Prioritized Experience Replay",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Replay Memory",
"parent": null
},
"name": "Prioritized Experience Replay",
"source_title": "Prioritized Experience Replay",
"source_url": "http://arxiv.org/abs/1511.05952v4"
},
{
"code_snippet_url": null,
"description": "**Experience Replay** is a replay memory technique used in reinforcement learning where we store the agent’s experiences at each time-step, $e\\_{t} = \\left(s\\_{t}, a\\_{t}, r\\_{t}, s\\_{t+1}\\right)$ in a data-set $D = e\\_{1}, \\cdots, e\\_{N}$ , pooled over many episodes into a replay memory. We then usually sample the memory randomly for a minibatch of experience, and use this to learn off-policy, as with Deep Q-Networks. This tackles the problem of autocorrelation leading to unstable training, by making the problem more like a supervised learning problem.\r\n\r\nImage Credit: [Hands-On Reinforcement Learning with Python, Sudharsan Ravichandiran](https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781788836524)",
"full_name": "Experience Replay",
"introduced_year": 1993,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Replay Memory",
"parent": null
},
"name": "Experience Replay",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/joint-estimation-of-age-and-gender-from
|
1806.02023
| null | null |
Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile Applications
|
Automatic age and gender classification based on unconstrained images has
become essential techniques on mobile devices. With limited computing power,
how to develop a robust system becomes a challenging task. In this paper, we
present an efficient convolutional neural network (CNN) called lightweight
multi-task CNN for simultaneous age and gender classification. Lightweight
multi-task CNN uses depthwise separable convolution to reduce the model size
and save the inference time. On the public challenging Adience dataset, the
accuracy of age and gender classification is better than baseline multi-task
CNN methods.
|
Automatic age and gender classification based on unconstrained images has become essential techniques on mobile devices.
|
http://arxiv.org/abs/1806.02023v1
|
http://arxiv.org/pdf/1806.02023v1.pdf
| null |
[
"Jia-Hong Lee",
"Yi-Ming Chan",
"Ting-Yen Chen",
"Chu-Song Chen"
] |
[
"Age And Gender Classification",
"Classification",
"Gender Classification",
"General Classification"
] | 2018-06-06T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "**Depthwise Convolution** is a type of convolution where we apply a single convolutional filter for each input channel. In the regular 2D [convolution](https://paperswithcode.com/method/convolution) performed over multiple input channels, the filter is as deep as the input and lets us freely mix channels to generate each element in the output. In contrast, depthwise convolutions keep each channel separate. To summarize the steps, we:\r\n\r\n1. Split the input and filter into channels.\r\n2. We convolve each input with the respective filter.\r\n3. We stack the convolved outputs together.\r\n\r\nImage Credit: [Chi-Feng Wang](https://towardsdatascience.com/a-basic-introduction-to-separable-convolutions-b99ec3102728)",
"full_name": "Depthwise Convolution",
"introduced_year": 2016,
"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": "Depthwise Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Pointwise Convolution** is a type of [convolution](https://paperswithcode.com/method/convolution) that uses a 1x1 kernel: a kernel that iterates through every single point. This kernel has a depth of however many channels the input image has. It can be used in conjunction with [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution) to produce an efficient class of convolutions known as [depthwise-separable convolutions](https://paperswithcode.com/method/depthwise-separable-convolution).\r\n\r\nImage Credit: [Chi-Feng Wang](https://towardsdatascience.com/a-basic-introduction-to-separable-convolutions-b99ec3102728)",
"full_name": "Pointwise Convolution",
"introduced_year": 2016,
"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": "Pointwise Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/kwotsin/TensorFlow-Xception/blob/c42ad8cab40733f9150711be3537243278612b22/xception.py#L67",
"description": "While [standard convolution](https://paperswithcode.com/method/convolution) performs the channelwise and spatial-wise computation in one step, **Depthwise Separable Convolution** splits the computation into two steps: [depthwise convolution](https://paperswithcode.com/method/depthwise-convolution) applies a single convolutional filter per each input channel and [pointwise convolution](https://paperswithcode.com/method/pointwise-convolution) is used to create a linear combination of the output of the depthwise convolution. The comparison of standard convolution and depthwise separable convolution is shown to the right.\r\n\r\nCredit: [Depthwise Convolution Is All You Need for Learning Multiple Visual Domains](https://paperswithcode.com/paper/depthwise-convolution-is-all-you-need-for)",
"full_name": "Depthwise Separable 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": "Depthwise Separable Convolution",
"source_title": "Xception: Deep Learning With Depthwise Separable Convolutions",
"source_url": "http://openaccess.thecvf.com/content_cvpr_2017/html/Chollet_Xception_Deep_Learning_CVPR_2017_paper.html"
},
{
"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
}
] |
https://paperswithcode.com/paper/a-peek-into-the-hidden-layers-of-a
|
1806.02012
| null | null |
A Peek Into the Hidden Layers of a Convolutional Neural Network Through a Factorization Lens
|
Despite their increasing popularity and success in a variety of supervised
learning problems, deep neural networks are extremely hard to interpret and
debug: Given and already trained Deep Neural Net, and a set of test inputs, how
can we gain insight into how those inputs interact with different layers of the
neural network? Furthermore, can we characterize a given deep neural network
based on it's observed behavior on different inputs? In this paper we propose a
novel factorization based approach on understanding how different deep neural
networks operate. In our preliminary results, we identify fascinating patterns
that link the factorization rank (typically used as a measure of
interestingness in unsupervised data analysis) with how well or poorly the deep
network has been trained. Finally, our proposed approach can help provide
visual insights on how high-level. interpretable patterns of the network's
input behave inside the hidden layers of the deep network.
| null |
http://arxiv.org/abs/1806.02012v1
|
http://arxiv.org/pdf/1806.02012v1.pdf
| null |
[
"Uday Singh Saini",
"Evangelos E. Papalexakis"
] |
[] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-algorithms-designs-for-networks
|
1806.02003
| null | null |
Deep Algorithms: designs for networks
|
A new design methodology for neural networks that is guided by traditional
algorithm design is presented. To prove our point, we present two heuristics
and demonstrate an algorithmic technique for incorporating additional weights
in their signal-flow graphs. We show that with training the performance of
these networks can not only exceed the performance of the initial network, but
can match the performance of more-traditional neural network architectures. A
key feature of our approach is that these networks are initialized with
parameters that provide a known performance threshold for the architecture on a
given task.
| null |
http://arxiv.org/abs/1806.02003v1
|
http://arxiv.org/pdf/1806.02003v1.pdf
| null |
[
"Abhejit Rajagopal",
"Shivkumar Chandrasekaran",
"Hrushikesh N. Mhaskar"
] |
[] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/an-explainable-adversarial-robustness-metric
|
1806.01477
| null | null |
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks
|
Deep Neural Networks(DNN) have excessively advanced the field of computer
vision by achieving state of the art performance in various vision tasks. These
results are not limited to the field of vision but can also be seen in speech
recognition and machine translation tasks. Recently, DNNs are found to poorly
fail when tested with samples that are crafted by making imperceptible changes
to the original input images. This causes a gap between the validation and
adversarial performance of a DNN. An effective and generalizable robustness
metric for evaluating the performance of DNN on these adversarial inputs is
still missing from the literature. In this paper, we propose Noise Sensitivity
Score (NSS), a metric that quantifies the performance of a DNN on a specific
input under different forms of fix-directional attacks. An insightful
mathematical explanation is provided for deeply understanding the proposed
metric. By leveraging the NSS, we also proposed a skewness based dataset
robustness metric for evaluating a DNN's adversarial performance on a given
dataset. Extensive experiments using widely used state of the art architectures
along with popular classification datasets, such as MNIST, CIFAR-10, CIFAR-100,
and ImageNet, are used to validate the effectiveness and generalization of our
proposed metrics. Instead of simply measuring a DNN's adversarial robustness in
the input domain, as previous works, the proposed NSS is built on top of
insightful mathematical understanding of the adversarial attack and gives a
more explicit explanation of the robustness.
| null |
http://arxiv.org/abs/1806.01477v2
|
http://arxiv.org/pdf/1806.01477v2.pdf
| null |
[
"Chirag Agarwal",
"Bo Dong",
"Dan Schonfeld",
"Anthony Hoogs"
] |
[
"Adversarial Attack",
"Adversarial Robustness",
"Deep Learning",
"Machine Translation",
"speech-recognition",
"Speech Recognition"
] | 2018-06-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-hierarchical-latent-vector-model-for
|
1803.05428
| null | null |
A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music
|
The Variational Autoencoder (VAE) has proven to be an effective model for producing semantically meaningful latent representations for natural data. However, it has thus far seen limited application to sequential data, and, as we demonstrate, existing recurrent VAE models have difficulty modeling sequences with long-term structure. To address this issue, we propose the use of a hierarchical decoder, which first outputs embeddings for subsequences of the input and then uses these embeddings to generate each subsequence independently. This structure encourages the model to utilize its latent code, thereby avoiding the "posterior collapse" problem, which remains an issue for recurrent VAEs. We apply this architecture to modeling sequences of musical notes and find that it exhibits dramatically better sampling, interpolation, and reconstruction performance than a "flat" baseline model. An implementation of our "MusicVAE" is available online at http://g.co/magenta/musicvae-code.
|
The Variational Autoencoder (VAE) has proven to be an effective model for producing semantically meaningful latent representations for natural data.
|
https://arxiv.org/abs/1803.05428v5
|
https://arxiv.org/pdf/1803.05428v5.pdf
|
ICML 2018 7
|
[
"Adam Roberts",
"Jesse Engel",
"Colin Raffel",
"Curtis Hawthorne",
"Douglas Eck"
] |
[
"Decoder"
] | 2018-03-13T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2420
|
http://proceedings.mlr.press/v80/roberts18a/roberts18a.pdf
|
a-hierarchical-latent-vector-model-for-1
| null |
[
{
"code_snippet_url": "",
"description": "In today’s digital age, Solana 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 Solana transaction not confirmed, your Solana wallet not showing balance, or you're trying to recover a lost Solana wallet, knowing where to get help is essential. That’s why the Solana 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 Solana Customer Support Number +1-833-534-1729\r\nSolana 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. Solana Transaction Not Confirmed\r\nOne of the most common concerns is when a Solana 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. Solana Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Solana wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Solana Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Solana wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Solana Deposit Not Received\r\nIf someone has sent you Solana but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Solana deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Solana Transaction Stuck or Pending\r\nSometimes your Solana transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Solana Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Solana wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Solana 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 Solana tech.\r\n\r\n24/7 Availability: Solana 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 Solana Support and Wallet Issues\r\nQ1: Can Solana support help me recover stolen BTC?\r\nA: While Solana 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: Solana 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 Solana’s official number (Solana is decentralized), it connects you to trained professionals experienced in resolving all major Solana issues.\r\n\r\nFinal Thoughts\r\nSolana 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 Solana transaction not confirmed, your Solana wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Solana 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": "Solana 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": "Solana Customer Service Number +1-833-534-1729",
"source_title": "Reducing the Dimensionality of Data with Neural Networks",
"source_url": "https://science.sciencemag.org/content/313/5786/504"
},
{
"code_snippet_url": "",
"description": "In today’s digital age, USD Coin 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 USD Coin transaction not confirmed, your USD Coin wallet not showing balance, or you're trying to recover a lost USD Coin wallet, knowing where to get help is essential. That’s why the USD Coin 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 USD Coin Customer Support Number +1-833-534-1729\r\nUSD Coin 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. USD Coin Transaction Not Confirmed\r\nOne of the most common concerns is when a USD Coin 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. USD Coin Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A USD Coin wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost USD Coin Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost USD Coin wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. USD Coin Deposit Not Received\r\nIf someone has sent you USD Coin but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A USD Coin deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. USD Coin Transaction Stuck or Pending\r\nSometimes your USD Coin transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. USD Coin Wallet Recovery Phrase Issue\r\nYour 12 or 24-word USD Coin wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the USD Coin 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 USD Coin tech.\r\n\r\n24/7 Availability: USD Coin 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 USD Coin Support and Wallet Issues\r\nQ1: Can USD Coin support help me recover stolen BTC?\r\nA: While USD Coin 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: USD Coin 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 USD Coin’s official number (USD Coin is decentralized), it connects you to trained professionals experienced in resolving all major USD Coin issues.\r\n\r\nFinal Thoughts\r\nUSD Coin 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 USD Coin transaction not confirmed, your USD Coin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the USD Coin 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": "USD Coin 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": "USD Coin Customer Service Number +1-833-534-1729",
"source_title": "Auto-Encoding Variational Bayes",
"source_url": "http://arxiv.org/abs/1312.6114v10"
}
] |
https://paperswithcode.com/paper/semiparametric-classification-of-forest
|
1806.01993
| null | null |
Beyond Trees: Classification with Sparse Pairwise Dependencies
|
Several classification methods assume that the underlying distributions follow tree-structured graphical models. Indeed, trees capture statistical dependencies between pairs of variables, which may be crucial to attain low classification errors. The resulting classifier is linear in the log-transformed univariate and bivariate densities that correspond to the tree edges. In practice, however, observed data may not be well approximated by trees. Yet, motivated by the importance of pairwise dependencies for accurate classification, here we propose to approximate the optimal decision boundary by a sparse linear combination of the univariate and bivariate log-transformed densities. Our proposed approach is semi-parametric in nature: we non-parametrically estimate the univariate and bivariate densities, remove pairs of variables that are nearly independent using the Hilbert-Schmidt independence criteria, and finally construct a linear SVM on the retained log-transformed densities. We demonstrate using both synthetic and real data that our resulting classifier, denoted SLB (Sparse Log-Bivariate density), is competitive with popular classification methods.
| null |
https://arxiv.org/abs/1806.01993v2
|
https://arxiv.org/pdf/1806.01993v2.pdf
| null |
[
"Yaniv Tenzer",
"Amit Moscovich",
"Mary Frances Dorn",
"Boaz Nadler",
"Clifford Spiegelman"
] |
[
"Classification",
"General Classification"
] | 2018-06-06T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "A **Support Vector Machine**, or **SVM**, is a non-parametric supervised learning model. For non-linear classification and regression, they utilise the kernel trick to map inputs to high-dimensional feature spaces. SVMs construct a hyper-plane or set of hyper-planes in a high or infinite dimensional space, which can be used for classification, regression or other tasks. Intuitively, a good separation is achieved by the hyper-plane that has the largest distance to the nearest training data points of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier. The figure to the right shows the decision function for a linearly separable problem, with three samples on the margin boundaries, called “support vectors”. \r\n\r\nSource: [scikit-learn](https://scikit-learn.org/stable/modules/svm.html)",
"full_name": "Support Vector Machine",
"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": "SVM",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/a-classification-based-study-of-covariate
|
1711.00970
| null | null |
A Classification-Based Study of Covariate Shift in GAN Distributions
|
A basic, and still largely unanswered, question in the context of Generative
Adversarial Networks (GANs) is whether they are truly able to capture all the
fundamental characteristics of the distributions they are trained on. In
particular, evaluating the diversity of GAN distributions is challenging and
existing methods provide only a partial understanding of this issue. In this
paper, we develop quantitative and scalable tools for assessing the diversity
of GAN distributions. Specifically, we take a classification-based perspective
and view loss of diversity as a form of covariate shift introduced by GANs. We
examine two specific forms of such shift: mode collapse and boundary
distortion. In contrast to prior work, our methods need only minimal human
supervision and can be readily applied to state-of-the-art GANs on large,
canonical datasets. Examining popular GANs using our tools indicates that these
GANs have significant problems in reproducing the more distributional
properties of their training dataset.
| null |
http://arxiv.org/abs/1711.00970v7
|
http://arxiv.org/pdf/1711.00970v7.pdf
|
ICML 2018 7
|
[
"Shibani Santurkar",
"Ludwig Schmidt",
"Aleksander Mądry"
] |
[
"Classification",
"Diversity",
"General Classification"
] | 2017-11-02T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2413
|
http://proceedings.mlr.press/v80/santurkar18a/santurkar18a.pdf
|
a-classification-based-study-of-covariate-1
| 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": "",
"description": "In today’s digital age, Dogecoin has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're trying to recover a lost Dogecoin wallet, knowing where to get help is essential. That’s why the Dogecoin customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Dogecoin Customer Support Number +1-833-534-1729\r\nDogecoin operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Dogecoin Transaction Not Confirmed\r\nOne of the most common concerns is when a Dogecoin transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Dogecoin Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Dogecoin wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Dogecoin Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Dogecoin wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Dogecoin Deposit Not Received\r\nIf someone has sent you Dogecoin but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Dogecoin deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Dogecoin Transaction Stuck or Pending\r\nSometimes your Dogecoin transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Dogecoin Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Dogecoin wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Dogecoin 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 Dogecoin tech.\r\n\r\n24/7 Availability: Dogecoin doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Dogecoin Support and Wallet Issues\r\nQ1: Can Dogecoin support help me recover stolen BTC?\r\nA: While Dogecoin transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Dogecoin transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Dogecoin’s official number (Dogecoin is decentralized), it connects you to trained professionals experienced in resolving all major Dogecoin issues.\r\n\r\nFinal Thoughts\r\nDogecoin is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Dogecoin customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Dogecoin Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Dogecoin Customer Service Number +1-833-534-1729",
"source_title": "Generative Adversarial Networks",
"source_url": "https://arxiv.org/abs/1406.2661v1"
}
] |
https://paperswithcode.com/paper/robust-structured-multi-task-multi-view
|
1806.01985
| null | null |
Robust Structured Multi-task Multi-view Sparse Tracking
|
Sparse representation is a viable solution to visual tracking. In this paper,
we propose a structured multi-task multi-view tracking (SMTMVT) method, which
exploits the sparse appearance model in the particle filter framework to track
targets under different challenges. Specifically, we extract features of the
target candidates from different views and sparsely represent them by a linear
combination of templates of different views. Unlike the conventional sparse
trackers, SMTMVT not only jointly considers the relationship between different
tasks and different views but also retains the structures among different views
in a robust multi-task multi-view formulation. We introduce a numerical
algorithm based on the proximal gradient method to quickly and effectively find
the sparsity by dividing the optimization problem into two subproblems with the
closed-form solutions. Both qualitative and quantitative evaluations on the
benchmark of challenging image sequences demonstrate the superior performance
of the proposed tracker against various state-of-the-art trackers.
| null |
http://arxiv.org/abs/1806.01985v1
|
http://arxiv.org/pdf/1806.01985v1.pdf
| null |
[
"Mohammadreza Javanmardi",
"Xiaojun Qi"
] |
[
"Visual Tracking"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/cluster-naturalistic-driving-encounters-using
|
1802.10214
| null | null |
Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning
|
Learning knowledge from driving encounters could help self-driving cars make
appropriate decisions when driving in complex settings with nearby vehicles
engaged. This paper develops an unsupervised classifier to group naturalistic
driving encounters into distinguishable clusters by combining an auto-encoder
with k-means clustering (AE-kMC). The effectiveness of AE-kMC was validated
using the data of 10,000 naturalistic driving encounters which were collected
by the University of Michigan, Ann Arbor in the past five years. We compare our
developed method with the $k$-means clustering methods and experimental results
demonstrate that the AE-kMC method outperforms the original k-means clustering
method.
| null |
http://arxiv.org/abs/1802.10214v2
|
http://arxiv.org/pdf/1802.10214v2.pdf
| null |
[
"Sisi Li",
"Wenshuo Wang",
"Zhaobin Mo",
"Ding Zhao"
] |
[
"Clustering",
"Self-Driving Cars"
] | 2018-02-28T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://cryptoabout.info",
"description": "**k-Means Clustering** is a clustering algorithm that divides a training set into $k$ different clusters of examples that are near each other. It works by initializing $k$ different centroids {$\\mu\\left(1\\right),\\ldots,\\mu\\left(k\\right)$} to different values, then alternating between two steps until convergence:\r\n\r\n(i) each training example is assigned to cluster $i$ where $i$ is the index of the nearest centroid $\\mu^{(i)}$\r\n\r\n(ii) each centroid $\\mu^{(i)}$ is updated to the mean of all training examples $x^{(j)}$ assigned to cluster $i$.\r\n\r\nText Source: Deep Learning, Goodfellow et al\r\n\r\nImage Source: [scikit-learn](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_digits.html)",
"full_name": "k-Means 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": "k-Means Clustering",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/reinforced-mnemonic-reader-for-machine
|
1705.02798
| null | null |
Reinforced Mnemonic Reader for Machine Reading Comprehension
|
In this paper, we introduce the Reinforced Mnemonic Reader for machine
reading comprehension tasks, which enhances previous attentive readers in two
aspects. First, a reattention mechanism is proposed to refine current
attentions by directly accessing to past attentions that are temporally
memorized in a multi-round alignment architecture, so as to avoid the problems
of attention redundancy and attention deficiency. Second, a new optimization
approach, called dynamic-critical reinforcement learning, is introduced to
extend the standard supervised method. It always encourages to predict a more
acceptable answer so as to address the convergence suppression problem occurred
in traditional reinforcement learning algorithms. Extensive experiments on the
Stanford Question Answering Dataset (SQuAD) show that our model achieves
state-of-the-art results. Meanwhile, our model outperforms previous systems by
over 6% in terms of both Exact Match and F1 metrics on two adversarial SQuAD
datasets.
|
In this paper, we introduce the Reinforced Mnemonic Reader for machine reading comprehension tasks, which enhances previous attentive readers in two aspects.
|
http://arxiv.org/abs/1705.02798v6
|
http://arxiv.org/pdf/1705.02798v6.pdf
| null |
[
"Minghao Hu",
"Yuxing Peng",
"Zhen Huang",
"Xipeng Qiu",
"Furu Wei",
"Ming Zhou"
] |
[
"Machine Reading Comprehension",
"Question Answering",
"Reading Comprehension",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2017-05-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/normalized-cut-with-adaptive-similarity-and
|
1806.01977
| null | null |
A Variational Image Segmentation Model based on Normalized Cut with Adaptive Similarity and Spatial Regularization
|
Image segmentation is a fundamental research topic in image processing and computer vision. In the last decades, researchers developed a large number of segmentation algorithms for various applications. Amongst these algorithms, the Normalized cut (Ncut) segmentation method is widely applied due to its good performance. The Ncut segmentation model is an optimization problem whose energy is defined on a specifically designed graph. Thus, the segmentation results of the existing Ncut method are largely dependent on a pre-constructed similarity measure on the graph since this measure is usually given empirically by users. This flaw will lead to some undesirable segmentation results. In this paper, we propose a Ncut-based segmentation algorithm by integrating an adaptive similarity measure and spatial regularization. The proposed model combines the Parzen-Rosenblatt window method, non-local weights entropy, Ncut energy, and regularizer of phase field in a variational framework. Our method can adaptively update the similarity measure function by estimating some parameters. This adaptive procedure enables the proposed algorithm finding a better similarity measure for classification than the Ncut method. We provide some mathematical interpretation of the proposed adaptive similarity from multi-viewpoints such as statistics and convex optimization. In addition, the regularizer of phase field can guarantee that the proposed algorithm has a robust performance in the presence of noise, and it can also rectify the similarity measure with a spatial priori. The well-posed theory such as the existence of the minimizer for the proposed model is given in the paper. Compared with some existing segmentation methods such as the traditional Ncut-based model and the classical Chan-Vese model, the numerical experiments show that our method can provide promising segmentation results.
| null |
https://arxiv.org/abs/1806.01977v3
|
https://arxiv.org/pdf/1806.01977v3.pdf
| null |
[
"Faqiang Wang",
"Cuicui Zhao",
"Jun Liu",
"Hai-yang Huang"
] |
[
"Clustering",
"Graph Clustering",
"Image Segmentation",
"Segmentation",
"Semantic Segmentation",
"Spectral Graph Clustering"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/distributionally-robust-submodular
|
1802.05249
| null | null |
Distributionally Robust Submodular Maximization
|
Submodular functions have applications throughout machine learning, but in
many settings, we do not have direct access to the underlying function $f$. We
focus on stochastic functions that are given as an expectation of functions
over a distribution $P$. In practice, we often have only a limited set of
samples $f_i$ from $P$. The standard approach indirectly optimizes $f$ by
maximizing the sum of $f_i$. However, this ignores generalization to the true
(unknown) distribution. In this paper, we achieve better performance on the
actual underlying function $f$ by directly optimizing a combination of bias and
variance. Algorithmically, we accomplish this by showing how to carry out
distributionally robust optimization (DRO) for submodular functions, providing
efficient algorithms backed by theoretical guarantees which leverage several
novel contributions to the general theory of DRO. We also show compelling
empirical evidence that DRO improves generalization to the unknown stochastic
submodular function.
| null |
http://arxiv.org/abs/1802.05249v2
|
http://arxiv.org/pdf/1802.05249v2.pdf
| null |
[
"Matthew Staib",
"Bryan Wilder",
"Stefanie Jegelka"
] |
[] | 2018-02-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/detecting-comma-shaped-clouds-for-severe
|
1802.08937
| null | null |
Detecting Comma-shaped Clouds for Severe Weather Forecasting using Shape and Motion
|
Meteorologists use shapes and movements of clouds in satellite images as
indicators of several major types of severe storms. Satellite imaginary data
are in increasingly higher resolution, both spatially and temporally, making it
impossible for humans to fully leverage the data in their forecast. Automatic
satellite imagery analysis methods that can find storm-related cloud patterns
as soon as they are detectable are in demand. We propose a machine learning and
pattern recognition based approach to detect "comma-shaped" clouds in satellite
images, which are specific cloud distribution patterns strongly associated with
the cyclone formulation. In order to detect regions with the targeted movement
patterns, our method is trained on manually annotated cloud examples
represented by both shape and motion-sensitive features. Sliding windows in
different scales are used to ensure that dense clouds will be captured, and we
implement effective selection rules to shrink the region of interest among
these sliding windows. Finally, we evaluate the method on a hold-out annotated
comma-shaped cloud dataset and cross-match the results with recorded storm
events in the severe weather database. The validated utility and accuracy of
our method suggest a high potential for assisting meteorologists in weather
forecasting.
| null |
http://arxiv.org/abs/1802.08937v3
|
http://arxiv.org/pdf/1802.08937v3.pdf
| null |
[
"Xinye Zheng",
"Jianbo Ye",
"Yukun Chen",
"Stephen Wistar",
"Jia Li",
"Jose A. Piedra-Fernández",
"Michael A. Steinberg",
"James Z. Wang"
] |
[
"Weather Forecasting"
] | 2018-02-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/graph-convolutional-neural-networks-for-web
|
1806.01973
| null | null |
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
|
Recent advancements in deep neural networks for graph-structured data have
led to state-of-the-art performance on recommender system benchmarks. However,
making these methods practical and scalable to web-scale recommendation tasks
with billions of items and hundreds of millions of users remains a challenge.
Here we describe a large-scale deep recommendation engine that we developed and
deployed at Pinterest. We develop a data-efficient Graph Convolutional Network
(GCN) algorithm PinSage, which combines efficient random walks and graph
convolutions to generate embeddings of nodes (i.e., items) that incorporate
both graph structure as well as node feature information. Compared to prior GCN
approaches, we develop a novel method based on highly efficient random walks to
structure the convolutions and design a novel training strategy that relies on
harder-and-harder training examples to improve robustness and convergence of
the model. We also develop an efficient MapReduce model inference algorithm to
generate embeddings using a trained model. We deploy PinSage at Pinterest and
train it on 7.5 billion examples on a graph with 3 billion nodes representing
pins and boards, and 18 billion edges. According to offline metrics, user
studies and A/B tests, PinSage generates higher-quality recommendations than
comparable deep learning and graph-based alternatives. To our knowledge, this
is the largest application of deep graph embeddings to date and paves the way
for a new generation of web-scale recommender systems based on graph
convolutional architectures.
|
We develop a data-efficient Graph Convolutional Network (GCN) algorithm PinSage, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i. e., items) that incorporate both graph structure as well as node feature information.
|
http://arxiv.org/abs/1806.01973v1
|
http://arxiv.org/pdf/1806.01973v1.pdf
| null |
[
"Rex Ying",
"Ruining He",
"Kai-Feng Chen",
"Pong Eksombatchai",
"William L. Hamilton",
"Jure Leskovec"
] |
[
"Recommendation Systems"
] | 2018-06-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/non-flat-ground-detection-based-on-a-local
|
1609.08436
| null | null |
Non-flat Ground Detection Based on A Local Descriptor
|
The detection of road and free space remains challenging for non-flat plane,
especially with the varying latitudinal and longitudinal slope or in the case
of multi-ground plane. In this paper, we propose a framework of the ground
plane detection with stereo vision. The main contribution of this paper is a
newly proposed descriptor which is implemented in the disparity image to obtain
a disparity texture image. The ground plane regions can be distinguished from
their surroundings effectively in the disparity texture image. Because the
descriptor is implemented in the local area of the image, it can address well
the problem of non-flat plane. And we also present a complete framework to
detect the ground plane regions base on the disparity texture image with
convolutional neural network architecture.
| null |
http://arxiv.org/abs/1609.08436v9
|
http://arxiv.org/pdf/1609.08436v9.pdf
| null |
[
"Kangru Wang",
"Lei Qu",
"Lili Chen",
"Yuzhang Gu",
"DongChen zhu",
"Xiaolin Zhang"
] |
[] | 2016-09-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/reverse-iterative-volume-sampling-for-linear
|
1806.01969
| null | null |
Reverse iterative volume sampling for linear regression
|
We study the following basic machine learning task: Given a fixed set of
$d$-dimensional input points for a linear regression problem, we wish to
predict a hidden response value for each of the points. We can only afford to
attain the responses for a small subset of the points that are then used to
construct linear predictions for all points in the dataset. The performance of
the predictions is evaluated by the total square loss on all responses (the
attained as well as the hidden ones). We show that a good approximate solution
to this least squares problem can be obtained from just dimension $d$ many
responses by using a joint sampling technique called volume sampling. Moreover,
the least squares solution obtained for the volume sampled subproblem is an
unbiased estimator of optimal solution based on all n responses. This
unbiasedness is a desirable property that is not shared by other common subset
selection techniques.
Motivated by these basic properties, we develop a theoretical framework for
studying volume sampling, resulting in a number of new matrix expectation
equalities and statistical guarantees which are of importance not only to least
squares regression but also to numerical linear algebra in general. Our methods
also lead to a regularized variant of volume sampling, and we propose the first
efficient algorithms for volume sampling which make this technique a practical
tool in the machine learning toolbox. Finally, we provide experimental evidence
which confirms our theoretical findings.
| null |
http://arxiv.org/abs/1806.01969v1
|
http://arxiv.org/pdf/1806.01969v1.pdf
| null |
[
"Michał Dereziński",
"Manfred K. Warmuth"
] |
[
"BIG-bench Machine Learning",
"regression"
] | 2018-06-06T00:00:00 | 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/a-reinforcement-learning-approach-to
|
1805.01553
| null | null |
A Reinforcement Learning Approach to Interactive-Predictive Neural Machine Translation
|
We present an approach to interactive-predictive neural machine translation
that attempts to reduce human effort from three directions: Firstly, instead of
requiring humans to select, correct, or delete segments, we employ the idea of
learning from human reinforcements in form of judgments on the quality of
partial translations. Secondly, human effort is further reduced by using the
entropy of word predictions as uncertainty criterion to trigger feedback
requests. Lastly, online updates of the model parameters after every
interaction allow the model to adapt quickly. We show in simulation experiments
that reward signals on partial translations significantly improve character
F-score and BLEU compared to feedback on full translations only, while human
effort can be reduced to an average number of $5$ feedback requests for every
input.
|
We present an approach to interactive-predictive neural machine translation that attempts to reduce human effort from three directions: Firstly, instead of requiring humans to select, correct, or delete segments, we employ the idea of learning from human reinforcements in form of judgments on the quality of partial translations.
|
http://arxiv.org/abs/1805.01553v3
|
http://arxiv.org/pdf/1805.01553v3.pdf
| null |
[
"Tsz Kin Lam",
"Julia Kreutzer",
"Stefan Riezler"
] |
[
"Machine Translation",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)",
"Translation"
] | 2018-05-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/efficient-hierarchical-robot-motion-planning
|
1802.04205
| null | null |
Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics
|
Noisy observations coupled with nonlinear dynamics pose one of the biggest
challenges in robot motion planning. By decomposing nonlinear dynamics into a
discrete set of local dynamics models, hybrid dynamics provide a natural way to
model nonlinear dynamics, especially in systems with sudden discontinuities in
dynamics due to factors such as contacts. We propose a hierarchical POMDP
planner that develops cost-optimized motion plans for hybrid dynamics models.
The hierarchical planner first develops a high-level motion plan to sequence
the local dynamics models to be visited and then converts it into a detailed
continuous state plan. This hierarchical planning approach results in a
decomposition of the POMDP planning problem into smaller sub-parts that can be
solved with significantly lower computational costs. The ability to sequence
the visitation of local dynamics models also provides a powerful way to
leverage the hybrid dynamics to reduce state uncertainty. We evaluate the
proposed planner on a navigation task in the simulated domain and on an
assembly task with a robotic manipulator, showing that our approach can solve
tasks having high observation noise and nonlinear dynamics effectively with
significantly lower computational costs compared to direct planning approaches.
|
This hierarchical planning approach results in a decomposition of the POMDP planning problem into smaller sub-parts that can be solved with significantly lower computational costs.
|
http://arxiv.org/abs/1802.04205v4
|
http://arxiv.org/pdf/1802.04205v4.pdf
| null |
[
"Ajinkya Jain",
"Scott Niekum"
] |
[
"Motion Planning"
] | 2018-02-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/spiking-linear-dynamical-systems-on
|
1805.08889
| null | null |
Spiking Linear Dynamical Systems on Neuromorphic Hardware for Low-Power Brain-Machine Interfaces
|
Neuromorphic architectures achieve low-power operation by using many simple
spiking neurons in lieu of traditional hardware. Here, we develop methods for
precise linear computations in spiking neural networks and use these methods to
map the evolution of a linear dynamical system (LDS) onto an existing
neuromorphic chip: IBM's TrueNorth. We analytically characterize, and
numerically validate, the discrepancy between the spiking LDS state sequence
and that of its non-spiking counterpart. These analytical results shed light on
the multiway tradeoff between time, space, energy, and accuracy in neuromorphic
computation. To demonstrate the utility of our work, we implemented a
neuromorphic Kalman filter (KF) and used it for offline decoding of human vocal
pitch from neural data. The neuromorphic KF could be used for low-power
filtering in domains beyond neuroscience, such as navigation or robotics.
| null |
http://arxiv.org/abs/1805.08889v2
|
http://arxiv.org/pdf/1805.08889v2.pdf
| null |
[
"David G. Clark",
"Jesse A. Livezey",
"Edward F. Chang",
"Kristofer E. Bouchard"
] |
[] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/competitive-multi-agent-inverse-reinforcement
|
1801.02124
| null | null |
Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations
|
This paper considers the problem of inverse reinforcement learning in
zero-sum stochastic games when expert demonstrations are known to be not
optimal. Compared to previous works that decouple agents in the game by
assuming optimality in expert strategies, we introduce a new objective function
that directly pits experts against Nash Equilibrium strategies, and we design
an algorithm to solve for the reward function in the context of inverse
reinforcement learning with deep neural networks as model approximations. In
our setting the model and algorithm do not decouple by agent. In order to find
Nash Equilibrium in large-scale games, we also propose an adversarial training
algorithm for zero-sum stochastic games, and show the theoretical appeal of
non-existence of local optima in its objective function. In our numerical
experiments, we demonstrate that our Nash Equilibrium and inverse reinforcement
learning algorithms address games that are not amenable to previous approaches
using tabular representations. Moreover, with sub-optimal expert demonstrations
our algorithms recover both reward functions and strategies with good quality.
|
Compared to previous works that decouple agents in the game by assuming optimality in expert strategies, we introduce a new objective function that directly pits experts against Nash Equilibrium strategies, and we design an algorithm to solve for the reward function in the context of inverse reinforcement learning with deep neural networks as model approximations.
|
http://arxiv.org/abs/1801.02124v2
|
http://arxiv.org/pdf/1801.02124v2.pdf
|
ICML 2018 7
|
[
"Xingyu Wang",
"Diego Klabjan"
] |
[
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-01-07T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2082
|
http://proceedings.mlr.press/v80/wang18d/wang18d.pdf
|
competitive-multi-agent-inverse-reinforcement-1
| null |
[] |
https://paperswithcode.com/paper/mining-for-meaning-from-vision-to-language
|
1806.01954
| null | null |
Mining for meaning: from vision to language through multiple networks consensus
|
Describing visual data into natural language is a very challenging task, at
the intersection of computer vision, natural language processing and machine
learning. Language goes well beyond the description of physical objects and
their interactions and can convey the same abstract idea in many ways. It is
both about content at the highest semantic level as well as about fluent form.
Here we propose an approach to describe videos in natural language by reaching
a consensus among multiple encoder-decoder networks. Finding such a consensual
linguistic description, which shares common properties with a larger group, has
a better chance to convey the correct meaning. We propose and train several
network architectures and use different types of image, audio and video
features. Each model produces its own description of the input video and the
best one is chosen through an efficient, two-phase consensus process. We
demonstrate the strength of our approach by obtaining state of the art results
on the challenging MSR-VTT dataset.
| null |
http://arxiv.org/abs/1806.01954v2
|
http://arxiv.org/pdf/1806.01954v2.pdf
| null |
[
"Iulia Duta",
"Andrei Liviu Nicolicioiu",
"Simion-Vlad Bogolin",
"Marius Leordeanu"
] |
[
"Decoder"
] | 2018-06-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/unbiased-estimates-for-linear-regression-via
|
1705.06908
| null | null |
Unbiased estimates for linear regression via volume sampling
|
Given a full rank matrix $X$ with more columns than rows, consider the task
of estimating the pseudo inverse $X^+$ based on the pseudo inverse of a sampled
subset of columns (of size at least the number of rows). We show that this is
possible if the subset of columns is chosen proportional to the squared volume
spanned by the rows of the chosen submatrix (ie, volume sampling). The
resulting estimator is unbiased and surprisingly the covariance of the
estimator also has a closed form: It equals a specific factor times
$X^{+\top}X^+$. Pseudo inverse plays an important part in solving the linear
least squares problem, where we try to predict a label for each column of $X$.
We assume labels are expensive and we are only given the labels for the small
subset of columns we sample from $X$. Using our methods we show that the weight
vector of the solution for the sub problem is an unbiased estimator of the
optimal solution for the whole problem based on all column labels. We believe
that these new formulas establish a fundamental connection between linear least
squares and volume sampling. We use our methods to obtain an algorithm for
volume sampling that is faster than state-of-the-art and for obtaining bounds
for the total loss of the estimated least-squares solution on all labeled
columns.
| null |
http://arxiv.org/abs/1705.06908v5
|
http://arxiv.org/pdf/1705.06908v5.pdf
|
NeurIPS 2017 12
|
[
"Michał Dereziński",
"Manfred K. Warmuth"
] |
[
"regression"
] | 2017-05-19T00:00:00 |
http://papers.nips.cc/paper/6901-unbiased-estimates-for-linear-regression-via-volume-sampling
|
http://papers.nips.cc/paper/6901-unbiased-estimates-for-linear-regression-via-volume-sampling.pdf
|
unbiased-estimates-for-linear-regression-via-1
| null |
[] |
https://paperswithcode.com/paper/reduced-order-modeling-through-machine
|
1806.01949
| null | null |
Reduced-Order Modeling through Machine Learning Approaches for Brittle Fracture Applications
|
In this paper, five different approaches for reduced-order modeling of
brittle fracture in geomaterials, specifically concrete, are presented and
compared. Four of the five methods rely on machine learning (ML) algorithms to
approximate important aspects of the brittle fracture problem. In addition to
the ML algorithms, each method incorporates different physics-based assumptions
in order to reduce the computational complexity while maintaining the physics
as much as possible. This work specifically focuses on using the ML approaches
to model a 2D concrete sample under low strain rate pure tensile loading
conditions with 20 preexisting cracks present. A high-fidelity finite
element-discrete element model is used to both produce a training dataset of
150 simulations and an additional 35 simulations for validation. Results from
the ML approaches are directly compared against the results from the
high-fidelity model. Strengths and weaknesses of each approach are discussed
and the most important conclusion is that a combination of physics-informed and
data-driven features are necessary for emulating the physics of crack
propagation, interaction and coalescence. All of the models presented here have
runtimes that are orders of magnitude faster than the original high-fidelity
model and pave the path for developing accurate reduced order models that could
be used to inform larger length-scale models with important sub-scale physics
that often cannot be accounted for due to computational cost.
| null |
http://arxiv.org/abs/1806.01949v1
|
http://arxiv.org/pdf/1806.01949v1.pdf
| null |
[
"A. Hunter",
"B. A. Moore",
"M. K. Mudunuru",
"V. T. Chau",
"R. L. Miller",
"R. B. Tchoua",
"C. Nyshadham",
"S. Karra",
"D. O. Malley",
"E. Rougier",
"H. S. Viswanathan",
"G. Srinivasan"
] |
[
"BIG-bench Machine Learning"
] | 2018-06-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-linear-time-method-for-the-detection-of
|
1806.01947
| null | null |
A linear time method for the detection of point and collective anomalies
|
The challenge of efficiently identifying anomalies in data sequences is an
important statistical problem that now arises in many applications. Whilst
there has been substantial work aimed at making statistical analyses robust to
outliers, or point anomalies, there has been much less work on detecting
anomalous segments, or collective anomalies, particularly in those settings
where point anomalies might also occur. In this article, we introduce
Collective And Point Anomalies (CAPA), a computationally efficient approach
that is suitable when collective anomalies are characterised by either a change
in mean, variance, or both, and distinguishes them from point anomalies.
Theoretical results establish the consistency of CAPA at detecting collective
anomalies and, as a by-product, the consistency of a popular penalised cost
based change in mean and variance detection method. Empirical results show that
CAPA has close to linear computational cost as well as being more accurate at
detecting and locating collective anomalies than other approaches. We
demonstrate the utility of CAPA through its ability to detect exoplanets from
light curve data from the Kepler telescope.
|
Theoretical results establish the consistency of CAPA at detecting collective anomalies and, as a by-product, the consistency of a popular penalised cost based change in mean and variance detection method.
|
http://arxiv.org/abs/1806.01947v2
|
http://arxiv.org/pdf/1806.01947v2.pdf
| null |
[
"Alexander T. M. Fisch",
"Idris A. Eckley",
"Paul Fearnhead"
] |
[] | 2018-06-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-to-understand-goal-specifications-by
|
1806.01946
| null |
H1xsSjC9Ym
|
Learning to Understand Goal Specifications by Modelling Reward
|
Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards. However, this places on environment designers the onus of designing language-conditional reward functions which may not be easily or tractably implemented as the complexity of the environment and the language scales. To overcome this limitation, we present a framework within which instruction-conditional RL agents are trained using rewards obtained not from the environment, but from reward models which are jointly trained from expert examples. As reward models improve, they learn to accurately reward agents for completing tasks for environment configurations---and for instructions---not present amongst the expert data. This framework effectively separates the representation of what instructions require from how they can be executed. In a simple grid world, it enables an agent to learn a range of commands requiring interaction with blocks and understanding of spatial relations and underspecified abstract arrangements. We further show the method allows our agent to adapt to changes in the environment without requiring new expert examples.
|
Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards.
|
https://arxiv.org/abs/1806.01946v4
|
https://arxiv.org/pdf/1806.01946v4.pdf
|
ICLR 2019 5
|
[
"Dzmitry Bahdanau",
"Felix Hill",
"Jan Leike",
"Edward Hughes",
"Arian Hosseini",
"Pushmeet Kohli",
"Edward Grefenstette"
] |
[
"Deep Reinforcement Learning",
"Reinforcement Learning"
] | 2018-06-05T00:00:00 |
https://openreview.net/forum?id=H1xsSjC9Ym
|
https://openreview.net/pdf?id=H1xsSjC9Ym
|
learning-to-understand-goal-specifications-by-1
| null |
[] |
https://paperswithcode.com/paper/towards-black-box-iterative-machine-teaching
|
1710.07742
| null | null |
Towards Black-box Iterative Machine Teaching
|
In this paper, we make an important step towards the black-box machine
teaching by considering the cross-space machine teaching, where the teacher and
the learner use different feature representations and the teacher can not fully
observe the learner's model. In such scenario, we study how the teacher is
still able to teach the learner to achieve faster convergence rate than the
traditional passive learning. We propose an active teacher model that can
actively query the learner (i.e., make the learner take exams) for estimating
the learner's status and provably guide the learner to achieve faster
convergence. The sample complexities for both teaching and query are provided.
In the experiments, we compare the proposed active teacher with the omniscient
teacher and verify the effectiveness of the active teacher model.
| null |
http://arxiv.org/abs/1710.07742v3
|
http://arxiv.org/pdf/1710.07742v3.pdf
|
ICML 2018 7
|
[
"Weiyang Liu",
"Bo Dai",
"Xingguo Li",
"Zhen Liu",
"James M. Rehg",
"Le Song"
] |
[] | 2017-10-21T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2398
|
http://proceedings.mlr.press/v80/liu18b/liu18b.pdf
|
towards-black-box-iterative-machine-teaching-1
| null |
[] |
https://paperswithcode.com/paper/supervised-learning-with-quantum-enhanced
|
1804.11326
| null | null |
Supervised learning with quantum enhanced feature spaces
|
Machine learning and quantum computing are two technologies each with the
potential for altering how computation is performed to address previously
untenable problems. Kernel methods for machine learning are ubiquitous for
pattern recognition, with support vector machines (SVMs) being the most
well-known method for classification problems. However, there are limitations
to the successful solution to such problems when the feature space becomes
large, and the kernel functions become computationally expensive to estimate. A
core element to computational speed-ups afforded by quantum algorithms is the
exploitation of an exponentially large quantum state space through controllable
entanglement and interference. Here, we propose and experimentally implement
two novel methods on a superconducting processor. Both methods represent the
feature space of a classification problem by a quantum state, taking advantage
of the large dimensionality of quantum Hilbert space to obtain an enhanced
solution. One method, the quantum variational classifier builds on [1,2] and
operates through using a variational quantum circuit to classify a training set
in direct analogy to conventional SVMs. In the second, a quantum kernel
estimator, we estimate the kernel function and optimize the classifier
directly. The two methods present a new class of tools for exploring the
applications of noisy intermediate scale quantum computers [3] to machine
learning.
|
Both methods represent the feature space of a classification problem by a quantum state, taking advantage of the large dimensionality of quantum Hilbert space to obtain an enhanced solution.
|
http://arxiv.org/abs/1804.11326v2
|
http://arxiv.org/pdf/1804.11326v2.pdf
| null |
[
"Vojtech Havlicek",
"Antonio D. Córcoles",
"Kristan Temme",
"Aram W. Harrow",
"Abhinav Kandala",
"Jerry M. Chow",
"Jay M. Gambetta"
] |
[
"BIG-bench Machine Learning",
"General Classification"
] | 2018-04-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/eigen-ecologically-inspired-genetic-approach
|
1806.01940
| null | null |
EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching from Scratch
|
Designing the structure of neural networks is considered one of the most
challenging tasks in deep learning, especially when there is few prior
knowledge about the task domain. In this paper, we propose an
Ecologically-Inspired GENetic (EIGEN) approach that uses the concept of
succession, extinction, mimicry, and gene duplication to search neural network
structure from scratch with poorly initialized simple network and few
constraints forced during the evolution, as we assume no prior knowledge about
the task domain. Specifically, we first use primary succession to rapidly
evolve a population of poorly initialized neural network structures into a more
diverse population, followed by a secondary succession stage for fine-grained
searching based on the networks from the primary succession. Extinction is
applied in both stages to reduce computational cost. Mimicry is employed during
the entire evolution process to help the inferior networks imitate the behavior
of a superior network and gene duplication is utilized to duplicate the learned
blocks of novel structures, both of which help to find better network
structures. Experimental results show that our proposed approach can achieve
similar or better performance compared to the existing genetic approaches with
dramatically reduced computation cost. For example, the network discovered by
our approach on CIFAR-100 dataset achieves 78.1% test accuracy under 120 GPU
hours, compared to 77.0% test accuracy in more than 65, 536 GPU hours in [35].
| null |
http://arxiv.org/abs/1806.01940v3
|
http://arxiv.org/pdf/1806.01940v3.pdf
|
CVPR 2019 6
|
[
"Jian Ren",
"Zhe Li",
"Jianchao Yang",
"Ning Xu",
"Tianbao Yang",
"David J. Foran"
] |
[
"GPU"
] | 2018-06-05T00:00:00 |
http://openaccess.thecvf.com/content_CVPR_2019/html/Ren_EIGEN_Ecologically-Inspired_GENetic_Approach_for_Neural_Network_Structure_Searching_From_CVPR_2019_paper.html
|
http://openaccess.thecvf.com/content_CVPR_2019/papers/Ren_EIGEN_Ecologically-Inspired_GENetic_Approach_for_Neural_Network_Structure_Searching_From_CVPR_2019_paper.pdf
|
eigen-ecologically-inspired-genetic-approach-1
| null |
[] |
https://paperswithcode.com/paper/exploring-feature-reuse-in-densenet
|
1806.01935
| null | null |
Exploring Feature Reuse in DenseNet Architectures
|
Densely Connected Convolutional Networks (DenseNets) have been shown to
achieve state-of-the-art results on image classification tasks while using
fewer parameters and computation than competing methods. Since each layer in
this architecture has full access to the feature maps of all previous layers,
the network is freed from the burden of having to relearn previously useful
features, thus alleviating issues with vanishing gradients. In this work we
explore the question: To what extent is it necessary to connect to all previous
layers in order to reap the benefits of feature reuse? To this end, we
introduce the notion of local dense connectivity and present evidence that less
connectivity, allowing for increased growth rate at a fixed network capacity,
can achieve a more efficient reuse of features and lead to higher accuracy in
dense architectures.
| null |
http://arxiv.org/abs/1806.01935v1
|
http://arxiv.org/pdf/1806.01935v1.pdf
| null |
[
"Andy Hess"
] |
[
"General Classification",
"image-classification",
"Image Classification"
] | 2018-06-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/explainable-neural-networks-based-on-additive
|
1806.01933
| null | null |
Explainable Neural Networks based on Additive Index Models
|
Machine Learning algorithms are increasingly being used in recent years due
to their flexibility in model fitting and increased predictive performance.
However, the complexity of the models makes them hard for the data analyst to
interpret the results and explain them without additional tools. This has led
to much research in developing various approaches to understand the model
behavior. In this paper, we present the Explainable Neural Network (xNN), a
structured neural network designed especially to learn interpretable features.
Unlike fully connected neural networks, the features engineered by the xNN can
be extracted from the network in a relatively straightforward manner and the
results displayed. With appropriate regularization, the xNN provides a
parsimonious explanation of the relationship between the features and the
output. We illustrate this interpretable feature--engineering property on
simulated examples.
| null |
http://arxiv.org/abs/1806.01933v1
|
http://arxiv.org/pdf/1806.01933v1.pdf
| null |
[
"Joel Vaughan",
"Agus Sudjianto",
"Erind Brahimi",
"Jie Chen",
"Vijayan N. Nair"
] |
[
"Feature Engineering"
] | 2018-06-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/peorl-integrating-symbolic-planning-and
|
1804.07779
| null | null |
PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making
|
Reinforcement learning and symbolic planning have both been used to build
intelligent autonomous agents. Reinforcement learning relies on learning from
interactions with real world, which often requires an unfeasibly large amount
of experience. Symbolic planning relies on manually crafted symbolic knowledge,
which may not be robust to domain uncertainties and changes. In this paper we
present a unified framework {\em PEORL} that integrates symbolic planning with
hierarchical reinforcement learning (HRL) to cope with decision-making in a
dynamic environment with uncertainties.
Symbolic plans are used to guide the agent's task execution and learning, and
the learned experience is fed back to symbolic knowledge to improve planning.
This method leads to rapid policy search and robust symbolic plans in complex
domains. The framework is tested on benchmark domains of HRL.
| null |
http://arxiv.org/abs/1804.07779v3
|
http://arxiv.org/pdf/1804.07779v3.pdf
| null |
[
"Fangkai Yang",
"Daoming Lyu",
"Bo Liu",
"Steven Gustafson"
] |
[
"Decision Making",
"Hierarchical Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-04-20T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/curriculum-domain-adaptation-for-semantic
|
1707.09465
| null | null |
Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes
|
During the last half decade, convolutional neural networks (CNNs) have
triumphed over semantic segmentation, which is one of the core tasks in many
applications such as autonomous driving. However, to train CNNs requires a
considerable amount of data, which is difficult to collect and laborious to
annotate. Recent advances in computer graphics make it possible to train CNNs
on photo-realistic synthetic imagery with computer-generated annotations.
Despite this, the domain mismatch between the real images and the synthetic
data cripples the models' performance. Hence, we propose a curriculum-style
learning approach to minimize the domain gap in urban scenery semantic
segmentation. The curriculum domain adaptation solves easy tasks first to infer
necessary properties about the target domain; in particular, the first task is
to learn global label distributions over images and local distributions over
landmark superpixels. These are easy to estimate because images of urban scenes
have strong idiosyncrasies (e.g., the size and spatial relations of buildings,
streets, cars, etc.). We then train a segmentation network while regularizing
its predictions in the target domain to follow those inferred properties. In
experiments, our method outperforms the baselines on two datasets and two
backbone networks. We also report extensive ablation studies about our
approach.
|
Hence, we propose a curriculum-style learning approach to minimize the domain gap in urban scenery semantic segmentation.
|
http://arxiv.org/abs/1707.09465v5
|
http://arxiv.org/pdf/1707.09465v5.pdf
|
ICCV 2017 10
|
[
"Yang Zhang",
"Philip David",
"Boqing Gong"
] |
[
"Autonomous Driving",
"Domain Adaptation",
"Image-to-Image Translation",
"Segmentation",
"Semantic Segmentation",
"Superpixels",
"Synthetic-to-Real Translation"
] | 2017-07-29T00:00:00 |
http://openaccess.thecvf.com/content_iccv_2017/html/Zhang_Curriculum_Domain_Adaptation_ICCV_2017_paper.html
|
http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhang_Curriculum_Domain_Adaptation_ICCV_2017_paper.pdf
|
curriculum-domain-adaptation-for-semantic-1
| null |
[] |
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