
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
Smarter applications are making better use of the insights gleaned from ...
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Recent Advances in Algorithmic HighDimensional Robust Statistics
Learning in the presence of outliers is a fundamental problem in statist...
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TOCO: A Framework for Compressing Neural Network Models Based on Tolerance Analysis
Neural network compression methods have enabled deploying large models o...
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Generating Semantic Adversarial Examples with Differentiable Rendering
Machine learning (ML) algorithms, especially deep neural networks, have ...
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AIGAN: AttackInspired Generation of Adversarial Examples
Adversarial examples that can fool deep models are mainly crafted by add...
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Convolutional Neural Nets: Foundations, Computations, and New Applications
We review mathematical foundations of convolutional neural nets (CNNs) w...
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On Need for Topology Awareness of Generative Models
Manifold assumption in learning states that: the data lie approximately ...
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Adversarial VCdimension and Sample Complexity of Neural Networks
Adversarial attacks during the testing phase of neural networks pose a c...
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TransMatch: A TransferLearning Scheme for SemiSupervised FewShot Learning
The successful application of deep learning to many visual recognition t...
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A New Basis for Sparse PCA
The statistical and computational performance of sparse principal compon...
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Flowbased Generative Models for Learning Manifold to Manifold Mappings
Many measurements or observations in computer vision and machine learnin...
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Transferring Multiscale Map Styles Using Generative Adversarial Networks
The advancement of the Artificial Intelligence (AI) technologies makes i...
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Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error
We study the L_1regularized maximum likelihood estimator/estimation (ML...
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Looking back to lowerlevel information in fewshot learning
Humans are capable of learning new concepts from small numbers of exampl...
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Model Extraction and Active Learning
Machine learning is being increasingly used by individuals, research ins...
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Inferring Signaling Pathways with Probabilistic Programming
Cells regulate themselves via dizzyingly complex biochemical processes c...
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Can Adversarial Weight Perturbations Inject Neural Backdoors?
Adversarial machine learning has exposed several security hazards of neu...
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Functional Regularization for Representation Learning: A Unified Theoretical Perspective
Unsupervised and selfsupervised learning approaches have become a cruci...
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Learning to Prune: Speeding up Repeated Computations
It is common to encounter situations where one must solve a sequence of ...
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Gradients as Features for Deep Representation Learning
We address the challenging problem of deep representation learning–the e...
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Gradual Network for Single Image Deraining
Most advances in single image deraining meet a key challenge, which is ...
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Interpretable and Accurate Finegrained Recognition via Region Grouping
We present an interpretable deep model for finegrained visual recogniti...
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BlurNet: Defense by Filtering the Feature Maps
Recently, the field of adversarial machine learning has been garnering a...
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Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin
We study the problem of properly learning large margin halfspaces in th...
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High Flux Passive Imaging with SinglePhoton Sensors
Singlephoton avalanche diodes (SPADs) are an emerging technology with a...
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Semi Supervised Phrase Localization in a Bidirectional CaptionImage Retrieval Framework
We introduce a novel deep neural network architecture that links visual ...
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Adversarially Robust Learning Could Leverage Computational Hardness
Over recent years, devising classification algorithms that are robust to...
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Differential Scene Flow from Light Field Gradients
This paper presents novel techniques for recovering 3D dense scene flow,...
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Robust Mean Estimation under Coordinatelevel Corruption
Data corruption, systematic or adversarial, may skew statistical estimat...
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Joint Reasoning for Temporal and Causal Relations
Understanding temporal and causal relations between events is a fundamen...
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Conservative Exploration using Interleaving
In many practical problems, a learning agent may want to learn the best ...
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DataDependent Differentially Private Parameter Learning for Directed Graphical Models
Directed graphical models (DGMs) are a class of probabilistic models tha...
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The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise
We study the computational complexity of adversarially robust proper lea...
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The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals
We study the problem of agnostic learning under the Gaussian distributio...
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Practical BlackBox Attacks against Machine Learning
Machine learning (ML) models, e.g., deep neural networks (DNNs), are vul...
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Neural Signatures for Licence Plate Reidentification
The problem of vehicle licence plate reidentification is generally cons...
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Finding Differentially Covarying Needles in a Temporally Evolving Haystack: A Scan Statistics Perspective
Recent results in coupled or temporal graphical models offer schemes for...
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Online Learning for Changing Environments using Coin Betting
A key challenge in online learning is that classical algorithms can be s...
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Analysis of Approximate Stochastic Gradient Using Quadratic Constraints and Sequential Semidefinite Programs
We present convergence rate analysis for the approximate stochastic grad...
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Calibrated BoostingForest
Excellent ranking power along with well calibrated probability estimates...
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Heat Kernel Smoothing in Irregular Image Domains
We present the discrete version of heat kernel smoothing on graph data s...
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The Unintended Consequences of Overfitting: Training Data Inference Attacks
Machine learning algorithms that are applied to sensitive data pose a di...
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A Deterministic Nonsmooth Frank Wolfe Algorithm with Coreset Guarantees
We present a new FrankWolfe (FW) type algorithm that is applicable to m...
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Neural Attribute Machines for Program Generation
Recurrent neural networks have achieved remarkable success at generating...
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The Limitations of Deep Learning in Adversarial Settings
Deep learning takes advantage of large datasets and computationally effi...
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Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
Deep learning algorithms have been shown to perform extremely well on ma...
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Analyzing the Robustness of Nearest Neighbors to Adversarial Examples
Motivated by applications such as autonomous vehicles, testtime attacks...
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Scalable Generalized Linear Bandits: Online Computation and Hashing
Generalized Linear Bandits (GLBs), a natural extension of the stochastic...
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Optimal Sample Complexity for Matrix Completion and Related Problems via ℓ_2Regularization
We study the strong duality of nonconvex matrix factorization: we show ...
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Algebraic Variety Models for HighRank Matrix Completion
We consider a generalization of lowrank matrix completion to the case w...
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