University of Wisconsin-Madison

research

∙ 02/12/2020

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 ...

Sebastian Raschka, et al. ∙

research

∙ 03/08/2022

Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild

Building reliable object detectors that can detect out-of-distribution (...

Xuefeng Du, et al. ∙

research

∙ 07/06/2021

Impact of On-Chip Interconnect on In-Memory Acceleration of Deep Neural Networks

With the widespread use of Deep Neural Networks (DNNs), machine learning...

Gokul Krishnan, et al. ∙

research

∙ 11/14/2019

Recent Advances in Algorithmic High-Dimensional Robust Statistics

Learning in the presence of outliers is a fundamental problem in statist...

Ilias Diakonikolas, et al. ∙

research

∙ 12/18/2019

TOCO: A Framework for Compressing Neural Network Models Based on Tolerance Analysis

Neural network compression methods have enabled deploying large models o...

Soroosh Khoram, et al. ∙

research

∙ 10/02/2019

Generating Semantic Adversarial Examples with Differentiable Rendering

Machine learning (ML) algorithms, especially deep neural networks, have ...

Lakshya Jain, et al. ∙

research

∙ 02/06/2020

AI-GAN: Attack-Inspired Generation of Adversarial Examples

Adversarial examples that can fool deep models are mainly crafted by add...

Tao Bai, et al. ∙

research

∙ 01/13/2021

Convolutional Neural Nets: Foundations, Computations, and New Applications

We review mathematical foundations of convolutional neural nets (CNNs) w...

Shengli Jiang, et al. ∙

research

∙ 09/07/2019

On Need for Topology Awareness of Generative Models

Manifold assumption in learning states that: the data lie approximately ...

Uyeong Jang, et al. ∙

research

∙ 12/18/2019

Adversarial VC-dimension and Sample Complexity of Neural Networks

Adversarial attacks during the testing phase of neural networks pose a c...

Zetong Qi, et al. ∙

research

∙ 12/19/2019

TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning

The successful application of deep learning to many visual recognition t...

Zhongjie Yu, et al. ∙

research

∙ 07/01/2020

A New Basis for Sparse PCA

The statistical and computational performance of sparse principal compon...

Fan Chen, et al. ∙

research

∙ 12/18/2020

Flow-based Generative Models for Learning Manifold to Manifold Mappings

Many measurements or observations in computer vision and machine learnin...

Xingjian Zhen, et al. ∙

research

∙ 10/21/2021

Generalized Out-of-Distribution Detection: A Survey

Out-of-distribution (OOD) detection is critical to ensuring the reliabil...

Jingkang Yang, et al. ∙

research

∙ 05/06/2019

Transferring Multiscale Map Styles Using Generative Adversarial Networks

The advancement of the Artificial Intelligence (AI) technologies makes i...

Yuhao Kang, et al. ∙

research

∙ 05/12/2020

Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error

We study the L_1-regularized maximum likelihood estimator/estimation (ML...

Sinong Geng, et al. ∙

research

∙ 05/27/2020

Looking back to lower-level information in few-shot learning

Humans are capable of learning new concepts from small numbers of exampl...

Zhongjie Yu, et al. ∙

research

∙ 08/14/2021

SIAM: Chiplet-based Scalable In-Memory Acceleration with Mesh for Deep Neural Networks

In-memory computing (IMC) on a monolithic chip for deep learning faces d...

Gokul Krishnan, et al. ∙

research

∙ 11/05/2018

Model Extraction and Active Learning

Machine learning is being increasingly used by individuals, research ins...

Varun Chandrasekaran, et al. ∙

research

∙ 04/06/2022

Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding

Applying methods in natural language processing on electronic health rec...

Yanjun Gao, et al. ∙

research

∙ 05/28/2020

Inferring Signaling Pathways with Probabilistic Programming

Cells regulate themselves via dizzyingly complex biochemical processes c...

David Merrell, et al. ∙

research

∙ 08/04/2020

Can Adversarial Weight Perturbations Inject Neural Backdoors?

Adversarial machine learning has exposed several security hazards of neu...

Siddhant Garg, et al. ∙

research

∙ 08/06/2020

Functional Regularization for Representation Learning: A Unified Theoretical Perspective

Unsupervised and self-supervised learning approaches have become a cruci...

Siddhant Garg, et al. ∙

research

∙ 08/27/2021

A Guide to Reproducible Research in Signal Processing and Machine Learning

Reproducibility is a growing problem that has been extensively studied a...

Joseph Shenouda, et al. ∙

research

∙ 02/02/2022

VOS: Learning What You Don't Know by Virtual Outlier Synthesis

Out-of-distribution (OOD) detection has received much attention lately d...

Xuefeng Du, et al. ∙

research

∙ 04/26/2019

Learning to Prune: Speeding up Repeated Computations

It is common to encounter situations where one must solve a sequence of ...

Daniel Alabi, et al. ∙

research

∙ 04/12/2020

Gradients as Features for Deep Representation Learning

We address the challenging problem of deep representation learning–the e...

Fangzhou Mu, et al. ∙

research

∙ 03/05/2021

Pufferfish: Communication-efficient Models At No Extra Cost

To mitigate communication overheads in distributed model training, sever...

Hongyi Wang, et al. ∙

research

∙ 07/12/2021

Forster Decomposition and Learning Halfspaces with Noise

A Forster transform is an operation that turns a distribution into one w...

Ilias Diakonikolas, et al. ∙

research

∙ 08/02/2021

Domain Adaptation for Autoencoder-Based End-to-End Communication Over Wireless Channels

The problem of domain adaptation conventionally considers the setting wh...

Jayaram Raghuram, et al. ∙

research

∙ 09/20/2019

Gradual Network for Single Image De-raining

Most advances in single image de-raining meet a key challenge, which is ...

Zhe Huang, et al. ∙

research

∙ 01/13/2022

Certifiable Robustness for Nearest Neighbor Classifiers

ML models are typically trained using large datasets of high quality. Ho...

Austen Z. Fan, et al. ∙

research

∙ 05/21/2020

Interpretable and Accurate Fine-grained Recognition via Region Grouping

We present an interpretable deep model for fine-grained visual recogniti...

Zixuan Huang, et al. ∙

research

∙ 08/30/2021

Equine Pain Behavior Classification via Self-Supervised Disentangled Pose Representation

Timely detection of horse pain is important for equine welfare. Horses e...

Maheen Rashid, et al. ∙

research

∙ 01/20/2022

Self-supervised Video Representation Learning with Cascade Positive Retrieval

Self-supervised video representation learning has been shown to effectiv...

Cheng-En Wu, et al. ∙

research

∙ 02/03/2022

SAFE-OCC: A Novelty Detection Framework for Convolutional Neural Network Sensors and its Application in Process Control

We present a novelty detection framework for Convolutional Neural Networ...

Joshua L. Pulsipher, et al. ∙

research

∙ 04/06/2022

End-to-End Instance Edge Detection

Edge detection has long been an important problem in the field of comput...

Xueyan Zou, et al. ∙

research

∙ 04/15/2022

Deep Unlearning via Randomized Conditionally Independent Hessians

Recent legislation has led to interest in machine unlearning, i.e., remo...

Ronak Mehta, et al. ∙

research

∙ 08/06/2019

BlurNet: Defense by Filtering the Feature Maps

Recently, the field of adversarial machine learning has been garnering a...

Ravi Raju, et al. ∙

research

∙ 08/29/2019

Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin

We study the problem of properly learning large margin halfspaces in th...

Ilias Diakonikolas, et al. ∙

research

∙ 07/31/2021

Structure Amplification on Multi-layer Stochastic Block Models

Much of the complexity of social, biological, and engineered systems ari...

Xiaodong Xin, et al. ∙

research

∙ 02/16/2022

ActionFormer: Localizing Moments of Actions with Transformers

Self-attention based Transformer models have demonstrated impressive res...

Chenlin Zhang, et al. ∙

research

∙ 02/26/2019

High Flux Passive Imaging with Single-Photon Sensors

Single-photon avalanche diodes (SPADs) are an emerging technology with a...

Atul Ingle, et al. ∙

research

∙ 05/18/2022

Constraining the Attack Space of Machine Learning Models with Distribution Clamping Preprocessing

Preprocessing and outlier detection techniques have both been applied to...

Ryan Feng, et al. ∙

research

∙ 08/08/2019

Semi Supervised Phrase Localization in a Bidirectional Caption-Image Retrieval Framework

We introduce a novel deep neural network architecture that links visual ...

Deepan Das, et al. ∙

research

∙ 11/14/2021

DEEP: DEnoising Entity Pre-training for Neural Machine Translation

It has been shown that machine translation models usually generate poor ...

Junjie Hu, et al. ∙

research

∙ 03/21/2022

Masked Discrimination for Self-Supervised Learning on Point Clouds

Masked autoencoding has achieved great success for self-supervised learn...

Haotian Liu, et al. ∙

research

∙ 06/10/2022

List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering

We study the problem of list-decodable sparse mean estimation. Specifica...

Ilias Diakonikolas, et al. ∙

research

∙ 05/28/2019

Adversarially Robust Learning Could Leverage Computational Hardness

Over recent years, devising classification algorithms that are robust to...

Sanjam Garg, et al. ∙

research

∙ 07/26/2019

Differential Scene Flow from Light Field Gradients

This paper presents novel techniques for recovering 3D dense scene flow,...

Sizhuo Ma, et al. ∙

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