
Efficient CoarsetoFine NonLocal Module for the Detection of Small Objects
An image is not just a collection of objects, but rather a graph where e...
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VQA with no questionsanswers training
Methods for teaching machines to answer visual questions have made signi...
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Expecting the Unexpected: Developing AutonomousSystem Design Principles for Reacting to Unpredicted Events and Conditions
When developing autonomous systems, engineers and other stakeholders mak...
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Towards a Computer Vision Particle Flow
In high energy physics experiments Particle Flow (PFlow) algorithms are ...
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Ensemble Wrapper Subsampling for Deep Modulation Classification
Subsampling of received wireless signals is important for relaxing hardw...
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Is Local SGD Better than Minibatch SGD?
We study local SGD (also known as parallel SGD and federated averaging),...
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How Good is SGD with Random Shuffling?
We study the performance of stochastic gradient descent (SGD) on smooth ...
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Understand, Compose and Respond  Answering Visual Questions by a Composition of Abstract Procedures
An image related question defines a specific visual task that is require...
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ImageNettrained deep neural network exhibits illusionlike response to the Scintillating Grid
Deep neural network (DNN) models for computer vision are now capable of ...
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Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution
We propose a learnedstructured unfolding neural network for the problem...
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DepthWidth Tradeoffs in Approximating Natural Functions with Neural Networks
We provide several new depthbased separation results for feedforward n...
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DistributionSpecific Hardness of Learning Neural Networks
Although neural networks are routinely and successfully trained in pract...
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Efficient Representation of LowDimensional Manifolds using Deep Networks
We consider the ability of deep neural networks to represent data that l...
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The Power of Depth for Feedforward Neural Networks
We show that there is a simple (approximately radial) function on ^d, ex...
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Limitations on VarianceReduction and Acceleration Schemes for Finite Sum Optimization
We study the conditions under which one is able to efficiently apply var...
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Bandit Regret Scaling with the Effective Loss Range
We study how the regret guarantees of nonstochastic multiarmed bandits ...
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Unsupervised Ensemble Regression
Consider a regression problem where there is no labeled data and the onl...
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Do You See What I Mean? Visual Resolution of Linguistic Ambiguities
Understanding language goes hand in hand with the ability to integrate c...
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Spurious Local Minima are Common in TwoLayer ReLU Neural Networks
We consider the optimization problem associated with training simple ReL...
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Human perception in computer vision
Computer vision has made remarkable progress in recent years. Deep neura...
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Graph Approximation and Clustering on a Budget
We consider the problem of learning from a similarity matrix (such as sp...
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WithoutReplacement Sampling for Stochastic Gradient Methods: Convergence Results and Application to Distributed Optimization
Stochastic gradient methods for machine learning and optimization proble...
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MultiPlayer Bandits  a Musical Chairs Approach
We consider a variant of the stochastic multiarmed bandit problem, wher...
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On the Quality of the Initial Basin in Overspecified Neural Networks
Deep learning, in the form of artificial neural networks, has achieved r...
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Convergence of Stochastic Gradient Descent for PCA
We consider the problem of principal component analysis (PCA) in a strea...
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An Algorithm for Training Polynomial Networks
We consider deep neural networks, in which the output of each node is a ...
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Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity
We study the convergence properties of the VRPCA algorithm introduced b...
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An Optimal Algorithm for Bandit and ZeroOrder Convex Optimization with TwoPoint Feedback
We consider the closely related problems of bandit convex optimization w...
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Communication Complexity of Distributed Convex Learning and Optimization
We study the fundamental limits to communicationefficient distributed m...
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Action Classification via Concepts and Attributes
Classes in natural images tend to follow long tail distributions. This i...
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On the Complexity of Learning with Kernels
A wellrecognized limitation of kernel learning is the requirement to ha...
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Attribute Efficient Linear Regression with DataDependent Sampling
In this paper we analyze a budgeted learning setting, in which the learn...
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Textual Features for Programming by Example
In Programming by Example, a system attempts to infer a program from inp...
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Nonstochastic MultiArmed Bandits with GraphStructured Feedback
We present and study a partialinformation model of online learning, whe...
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On the Optimality of Averaging in Distributed Statistical Learning
A common approach to statistical learning with bigdata is to randomly s...
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Facespace Action Recognition by FaceObject Interactions
Action recognition in still images has seen major improvement in recent ...
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HandObject Interaction and Precise Localization in Transitive Action Recognition
Action recognition in still images has seen major improvement in recent ...
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Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation
Many machine learning approaches are characterized by information constr...
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A Quantitative Version of the GibbardSatterthwaite Theorem for Three Alternatives
The GibbardSatterthwaite theorem states that every nondictatorial elec...
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Efficient Classification for Metric Data
Recent advances in largemargin classification of data residing in gener...
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Adaptive Metric Dimensionality Reduction
We study adaptive datadependent dimensionality reduction in the context...
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On the Complexity of Bandit and DerivativeFree Stochastic Convex Optimization
The problem of stochastic convex optimization with bandit feedback (in t...
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Why are images smooth?
It is a well observed phenomenon that natural images are smooth, in the ...
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Stable Camera Motion Estimation Using Convex Programming
We study the inverse problem of estimating n locations t_1, ..., t_n (up...
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Manifold Learning: The Price of Normalization
We analyze the performance of a class of manifoldlearning algorithms th...
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From Shading to Local Shape
We develop a framework for extracting a concise representation of the sh...
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A Unified Multiscale Framework for Discrete Energy Minimization
Discrete energy minimization is a ubiquitous task in computer vision, ye...
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Linear versus Nonlinear Acquisition of StepFunctions
We address in this paper the following two closely related problems: 1...
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Approximation of Polyhedral Surface Uniformization
We present a constructive approach for approximating the conformal map (...
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Distributed Computing Made Secure: A New Cycle Cover Theorem
In the area of distributed graph algorithms a number of network's entiti...
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Weizmann Institute of Science
The Weizmann Institute of Science is a public research university in Rehovot, Israel, established in 1934, 14 years before the State of Israel.