
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 ...
read it

Impact of OnChip Interconnect on InMemory Acceleration of Deep Neural Networks
With the widespread use of Deep Neural Networks (DNNs), machine learning...
read it

Recent Advances in Algorithmic HighDimensional Robust Statistics
Learning in the presence of outliers is a fundamental problem in statist...
read it

TOCO: A Framework for Compressing Neural Network Models Based on Tolerance Analysis
Neural network compression methods have enabled deploying large models o...
read it

Generating Semantic Adversarial Examples with Differentiable Rendering
Machine learning (ML) algorithms, especially deep neural networks, have ...
read it

AIGAN: AttackInspired Generation of Adversarial Examples
Adversarial examples that can fool deep models are mainly crafted by add...
read it

Convolutional Neural Nets: Foundations, Computations, and New Applications
We review mathematical foundations of convolutional neural nets (CNNs) w...
read it

On Need for Topology Awareness of Generative Models
Manifold assumption in learning states that: the data lie approximately ...
read it

Adversarial VCdimension and Sample Complexity of Neural Networks
Adversarial attacks during the testing phase of neural networks pose a c...
read it

TransMatch: A TransferLearning Scheme for SemiSupervised FewShot Learning
The successful application of deep learning to many visual recognition t...
read it

A New Basis for Sparse PCA
The statistical and computational performance of sparse principal compon...
read it

Flowbased Generative Models for Learning Manifold to Manifold Mappings
Many measurements or observations in computer vision and machine learnin...
read it

Transferring Multiscale Map Styles Using Generative Adversarial Networks
The advancement of the Artificial Intelligence (AI) technologies makes i...
read it

Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error
We study the L_1regularized maximum likelihood estimator/estimation (ML...
read it

Looking back to lowerlevel information in fewshot learning
Humans are capable of learning new concepts from small numbers of exampl...
read it

Model Extraction and Active Learning
Machine learning is being increasingly used by individuals, research ins...
read it

Inferring Signaling Pathways with Probabilistic Programming
Cells regulate themselves via dizzyingly complex biochemical processes c...
read it

Can Adversarial Weight Perturbations Inject Neural Backdoors?
Adversarial machine learning has exposed several security hazards of neu...
read it

Functional Regularization for Representation Learning: A Unified Theoretical Perspective
Unsupervised and selfsupervised learning approaches have become a cruci...
read it

Learning to Prune: Speeding up Repeated Computations
It is common to encounter situations where one must solve a sequence of ...
read it

Gradients as Features for Deep Representation Learning
We address the challenging problem of deep representation learning–the e...
read it

Pufferfish: Communicationefficient Models At No Extra Cost
To mitigate communication overheads in distributed model training, sever...
read it

Forster Decomposition and Learning Halfspaces with Noise
A Forster transform is an operation that turns a distribution into one w...
read it

Gradual Network for Single Image Deraining
Most advances in single image deraining meet a key challenge, which is ...
read it

Interpretable and Accurate Finegrained Recognition via Region Grouping
We present an interpretable deep model for finegrained visual recogniti...
read it

BlurNet: Defense by Filtering the Feature Maps
Recently, the field of adversarial machine learning has been garnering a...
read it

Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin
We study the problem of properly learning large margin halfspaces in th...
read it

High Flux Passive Imaging with SinglePhoton Sensors
Singlephoton avalanche diodes (SPADs) are an emerging technology with a...
read it

Semi Supervised Phrase Localization in a Bidirectional CaptionImage Retrieval Framework
We introduce a novel deep neural network architecture that links visual ...
read it

Adversarially Robust Learning Could Leverage Computational Hardness
Over recent years, devising classification algorithms that are robust to...
read it

Differential Scene Flow from Light Field Gradients
This paper presents novel techniques for recovering 3D dense scene flow,...
read it

Robust Mean Estimation under Coordinatelevel Corruption
Data corruption, systematic or adversarial, may skew statistical estimat...
read it

Joint Reasoning for Temporal and Causal Relations
Understanding temporal and causal relations between events is a fundamen...
read it

Conservative Exploration using Interleaving
In many practical problems, a learning agent may want to learn the best ...
read it

A Shuffling Framework for Local Differential Privacy
ldp deployments are vulnerable to inference attacks as an adversary can ...
read it

DataDependent Differentially Private Parameter Learning for Directed Graphical Models
Directed graphical models (DGMs) are a class of probabilistic models tha...
read it

The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise
We study the computational complexity of adversarially robust proper lea...
read it

The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals
We study the problem of agnostic learning under the Gaussian distributio...
read it

Practical BlackBox Attacks against Machine Learning
Machine learning (ML) models, e.g., deep neural networks (DNNs), are vul...
read it

Neural Signatures for Licence Plate Reidentification
The problem of vehicle licence plate reidentification is generally cons...
read it

Finding Differentially Covarying Needles in a Temporally Evolving Haystack: A Scan Statistics Perspective
Recent results in coupled or temporal graphical models offer schemes for...
read it

Online Learning for Changing Environments using Coin Betting
A key challenge in online learning is that classical algorithms can be s...
read it

Analysis of Approximate Stochastic Gradient Using Quadratic Constraints and Sequential Semidefinite Programs
We present convergence rate analysis for the approximate stochastic grad...
read it

Calibrated BoostingForest
Excellent ranking power along with well calibrated probability estimates...
read it

Heat Kernel Smoothing in Irregular Image Domains
We present the discrete version of heat kernel smoothing on graph data s...
read it

The Unintended Consequences of Overfitting: Training Data Inference Attacks
Machine learning algorithms that are applied to sensitive data pose a di...
read it

A Deterministic Nonsmooth Frank Wolfe Algorithm with Coreset Guarantees
We present a new FrankWolfe (FW) type algorithm that is applicable to m...
read it

Neural Attribute Machines for Program Generation
Recurrent neural networks have achieved remarkable success at generating...
read it

The Limitations of Deep Learning in Adversarial Settings
Deep learning takes advantage of large datasets and computationally effi...
read it

Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
Deep learning algorithms have been shown to perform extremely well on ma...
read it
University of WisconsinMadison
The University of Wisconsin–Madison is a public landgrant university and prolific research institution. Our students, staff, and faculty members partake in a worldclass education and solve realworld problems.