Princeton University

research

∙ 03/23/2022

Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

Many physical systems can be best understood as sets of discrete data wi...

Savannah Thais, et al. ∙

research

∙ 05/21/2020

Can Shallow Neural Networks Beat the Curse of Dimensionality? A mean field training perspective

We prove that the gradient descent training of a two-layer neural networ...

Stephan Wojtowytsch, et al. ∙

research

∙ 03/11/2020

A Mean-field Analysis of Deep ResNet and Beyond: Towards Provable Optimization Via Overparameterization From Depth

Training deep neural networks with stochastic gradient descent (SGD) can...

Yiping Lu, et al. ∙

research

∙ 11/12/2019

Machine Intelligence at the Edge with Learning Centric Power Allocation

While machine-type communication (MTC) devices generate considerable amo...

Shuai Wang, et al. ∙

research

∙ 02/25/2019

A Theoretical Analysis of Contrastive Unsupervised Representation Learning

Recent empirical works have successfully used unlabeled data to learn fe...

Sanjeev Arora, et al. ∙

research

∙ 11/10/2019

Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering

This paper presents a general approach for open-domain question answerin...

Sewon Min, et al. ∙

research

∙ 07/26/2020

OASIS: A Large-Scale Dataset for Single Image 3D in the Wild

Single-view 3D is the task of recovering 3D properties such as depth and...

Weifeng Chen, et al. ∙

research

∙ 03/15/2021

Pushing the Limits of Capsule Networks

Convolutional neural networks use pooling and other downscaling operatio...

Prem Nair, et al. ∙

research

∙ 12/18/2019

Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion

We introduce DeepInversion, a new method for synthesizing images from th...

Hongxu Yin, et al. ∙

research

∙ 07/16/2020

SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design

Parametric computer-aided design (CAD) is the dominant paradigm in mecha...

Ari Seff, et al. ∙

research

∙ 04/08/2019

A Comparative Analysis of the Optimization and Generalization Property of Two-layer Neural Network and Random Feature Models Under Gradient Descent Dynamics

A fairly comprehensive analysis is presented for the gradient descent dy...

Weinan E, et al. ∙

research

∙ 05/21/2020

Kolmogorov Width Decay and Poor Approximators in Machine Learning: Shallow Neural Networks, Random Feature Models and Neural Tangent Kernels

We establish a scale separation of Kolmogorov width type between subspac...

Weinan E, et al. ∙

research

∙ 12/10/2018

Theoretical Analysis of Auto Rate-Tuning by Batch Normalization

Batch Normalization (BN) has become a cornerstone of deep learning acros...

Sanjeev Arora, et al. ∙

research

∙ 02/09/2022

Can Humans Do Less-Than-One-Shot Learning?

Being able to learn from small amounts of data is a key characteristic o...

Maya Malaviya, et al. ∙

research

∙ 12/11/2018

DeepV2D: Video to Depth with Differentiable Structure from Motion

We propose DeepV2D, an end-to-end differentiable deep learning architect...

Zachary Teed, et al. ∙

research

∙ 05/05/2019

Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples

A large body of recent work has investigated the phenomenon of evasion a...

Vikash Sehwag, et al. ∙

research

∙ 03/06/2022

HEAR 2021: Holistic Evaluation of Audio Representations

What audio embedding approach generalizes best to a wide range of downst...

Joseph Turian, et al. ∙

research

∙ 01/24/2019

Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks

Recent works have cast some light on the mystery of why deep nets fit an...

Sanjeev Arora, et al. ∙

research

∙ 05/24/2019

Sequential Gaussian Processes for Online Learning of Nonstationary Functions

Many machine learning problems can be framed in the context of estimatin...

Michael Minyi Zhang, et al. ∙

research

∙ 02/20/2020

Second Order Optimization Made Practical

Optimization in machine learning, both theoretical and applied, is prese...

Rohan Anil, et al. ∙

research

∙ 12/13/2021

BScNets: Block Simplicial Complex Neural Networks

Simplicial neural networks (SNN) have recently emerged as the newest dir...

Yuzhou Chen, et al. ∙

research

∙ 03/02/2022

Adaptive Gradient Methods with Local Guarantees

Adaptive gradient methods are the method of choice for optimization in m...

Zhou Lu, et al. ∙

research

∙ 04/10/2019

Analysis of the Gradient Descent Algorithm for a Deep Neural Network Model with Skip-connections

The behavior of the gradient descent (GD) algorithm is analyzed for a de...

Weinan E, et al. ∙

research

∙ 11/27/2019

The Nonstochastic Control Problem

We consider the problem of controlling an unknown linear dynamical syste...

Elad Hazan, et al. ∙

research

∙ 11/01/2021

On the Expressivity of Markov Reward

Reward is the driving force for reinforcement-learning agents. This pape...

David Abel, et al. ∙

research

∙ 04/22/2019

Tracking and Improving Information in the Service of Fairness

As algorithmic prediction systems have become widespread, fears that the...

Sumegha Garg, et al. ∙

research

∙ 09/25/2019

HaarPooling: Graph Pooling with Compressive Haar Basis

Deep Graph Neural Networks (GNNs) are instrumental in graph classificati...

Yu Guang Wang, et al. ∙

research

∙ 04/01/2020

Physically Realizable Adversarial Examples for LiDAR Object Detection

Modern autonomous driving systems rely heavily on deep learning models t...

James Tu, et al. ∙

research

∙ 01/18/2021

Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation

Federated learning (FL), as a distributed machine learning paradigm, pro...

Jun Li, et al. ∙

research

∙ 02/12/2021

Machine Learning for Mechanical Ventilation Control

We consider the problem of controlling an invasive mechanical ventilator...

Daniel Suo, et al. ∙

research

∙ 11/25/2021

Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networks

One major goal of the AI security community is to securely and reliably ...

Xiangyu Qi, et al. ∙

research

∙ 06/18/2019

Poisoning Attacks with Generative Adversarial Nets

Machine learning algorithms are vulnerable to poisoning attacks: An adve...

Luis Muñoz-González, et al. ∙

research

∙ 10/11/2019

DiabDeep: Pervasive Diabetes Diagnosis based on Wearable Medical Sensors and Efficient Neural Networks

Diabetes impacts the quality of life of millions of people. However, dia...

Hongxu Yin, et al. ∙

research

∙ 03/17/2020

Nonparametric Deconvolution Models

We describe nonparametric deconvolution models (NDMs), a family of Bayes...

Allison J. B. Chaney, et al. ∙

research

∙ 07/04/2020

RDP-GAN: A Rényi-Differential Privacy based Generative Adversarial Network

Generative adversarial network (GAN) has attracted increasing attention ...

Chuan Ma, et al. ∙

research

∙ 07/29/2019

A Mathematical Model for Linguistic Universals

Inspired by chemical kinetics and neurobiology, we propose a mathematica...

Weinan E, et al. ∙

research

∙ 08/29/2019

Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity

In this paper, we settle the sampling complexity of solving discounted t...

Aaron Sidford, et al. ∙

research

∙ 03/07/2020

Machine learning based non-Newtonian fluid model with molecular fidelity

We introduce a machine-learning-based framework for constructing continu...

Huan Lei, et al. ∙

research

∙ 12/22/2021

ProBF: Learning Probabilistic Safety Certificates with Barrier Functions

Safety-critical applications require controllers/policies that can guara...

Athindran Ramesh Kumar, et al. ∙

research

∙ 01/31/2022

BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression

Communication efficiency has been widely recognized as the bottleneck fo...

Haoyu Zhao, et al. ∙

research

∙ 07/11/2020

Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation

The ability to perform effective planning is crucial for building an ins...

Zhiwei Deng, et al. ∙

research

∙ 09/08/2021

EMA: Auditing Data Removal from Trained Models

Data auditing is a process to verify whether certain data have been remo...

Yangsibo Huang, et al. ∙

research

∙ 12/21/2021

Adversarial Neural Networks for Error Correcting Codes

Error correcting codes are a fundamental component in modern day communi...

Hung T. Nguyen, et al. ∙

research

∙ 02/18/2022

A Molecular Prior Distribution for Bayesian Inference Based on Wilson Statistics

Background and Objective: Wilson statistics describe well the power spec...

Marc Aurèle Gilles, et al. ∙

research

∙ 02/27/2022

Bayesian Active Learning for Discrete Latent Variable Models

Active learning seeks to reduce the number of samples required to estima...

Aditi Jha, et al. ∙

research

∙ 05/08/2022

Decentralized Stochastic Optimization with Inherent Privacy Protection

Decentralized stochastic optimization is the basic building block of mod...

Yongqiang Wang, et al. ∙

research

∙ 12/19/2018

D3D: Distilled 3D Networks for Video Action Recognition

State-of-the-art methods for video action recognition commonly use an en...

Jonathan C. Stroud, et al. ∙

research

∙ 03/18/2019

Annealing for Distributed Global Optimization

The paper proves convergence to global optima for a class of distributed...

Brian Swenson, et al. ∙

research

∙ 01/14/2020

Bio-Inspired Hashing for Unsupervised Similarity Search

The fruit fly Drosophila's olfactory circuit has inspired a new locality...

Chaitanya K. Ryali, et al. ∙

research

∙ 06/29/2020

Path Integral Based Convolution and Pooling for Graph Neural Networks

Graph neural networks (GNNs) extends the functionality of traditional ne...

Zheng Ma, et al. ∙

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