Princeton University

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Princeton University is a vibrant community of scholarship and learning that stands in the nation's service and in the service of all nations.

Featured Researchers

H. Vincent Poor
192 publications
Raquel Urtasun
128 publications
Yi Zhang
115 publications
Han Liu
102 publications
David M. Blei
76 publications
Zhaoran Wang
71 publications
Yang Feng
67 publications
Yuxin Chen
64 publications
Zhuoran Yang
60 publications
Jason D. Lee
59 publications
Tengyu Ma
57 publications

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...
05/21/2020 ∙ by Stephan Wojtowytsch, et al. ∙ 51 ∙ share

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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...
03/11/2020 ∙ by Yiping Lu, et al. ∙ 42 ∙ share

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Machine Intelligence at the Edge with Learning Centric Power Allocation

While machine-type communication (MTC) devices generate considerable amo...
11/12/2019 ∙ by Shuai Wang, et al. ∙ 38 ∙ share

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A Theoretical Analysis of Contrastive Unsupervised Representation Learning

Recent empirical works have successfully used unlabeled data to learn fe...
02/25/2019 ∙ by Sanjeev Arora, et al. ∙ 34 ∙ share

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Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering

This paper presents a general approach for open-domain question answerin...
11/10/2019 ∙ by Sewon Min, et al. ∙ 30 ∙ share

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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...
07/26/2020 ∙ by Weifeng Chen, et al. ∙ 30 ∙ share

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Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion

We introduce DeepInversion, a new method for synthesizing images from th...
12/18/2019 ∙ by Hongxu Yin, et al. ∙ 27 ∙ share

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

Parametric computer-aided design (CAD) is the dominant paradigm in mecha...
07/16/2020 ∙ by Ari Seff, et al. ∙ 26 ∙ share

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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...
04/08/2019 ∙ by Weinan E, et al. ∙ 25 ∙ share

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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...
05/21/2020 ∙ by Weinan E, et al. ∙ 22 ∙ share

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Theoretical Analysis of Auto Rate-Tuning by Batch Normalization

Batch Normalization (BN) has become a cornerstone of deep learning acros...
12/10/2018 ∙ by Sanjeev Arora, et al. ∙ 20 ∙ share

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DeepV2D: Video to Depth with Differentiable Structure from Motion

We propose DeepV2D, an end-to-end differentiable deep learning architect...
12/11/2018 ∙ by Zachary Teed, et al. ∙ 18 ∙ share

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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...
05/05/2019 ∙ by Vikash Sehwag, et al. ∙ 18 ∙ share

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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...
01/24/2019 ∙ by Sanjeev Arora, et al. ∙ 16 ∙ share

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Sequential Gaussian Processes for Online Learning of Nonstationary Functions

Many machine learning problems can be framed in the context of estimatin...
05/24/2019 ∙ by Michael Minyi Zhang, et al. ∙ 16 ∙ share

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Second Order Optimization Made Practical

Optimization in machine learning, both theoretical and applied, is prese...
02/20/2020 ∙ by Rohan Anil, et al. ∙ 16 ∙ share

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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...
04/10/2019 ∙ by Weinan E, et al. ∙ 15 ∙ share

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The Nonstochastic Control Problem

We consider the problem of controlling an unknown linear dynamical syste...
11/27/2019 ∙ by Elad Hazan, et al. ∙ 15 ∙ share

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Tracking and Improving Information in the Service of Fairness

As algorithmic prediction systems have become widespread, fears that the...
04/22/2019 ∙ by Sumegha Garg, et al. ∙ 14 ∙ share

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Physically Realizable Adversarial Examples for LiDAR Object Detection

Modern autonomous driving systems rely heavily on deep learning models t...
04/01/2020 ∙ by James Tu, et al. ∙ 14 ∙ share

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

Federated learning (FL), as a distributed machine learning paradigm, pro...
01/18/2021 ∙ by Jun Li, et al. ∙ 14 ∙ share

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Machine Learning for Mechanical Ventilation Control

We consider the problem of controlling an invasive mechanical ventilator...
02/12/2021 ∙ by Daniel Suo, et al. ∙ 14 ∙ share

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Poisoning Attacks with Generative Adversarial Nets

Machine learning algorithms are vulnerable to poisoning attacks: An adve...
06/18/2019 ∙ by Luis Muñoz-González, et al. ∙ 13 ∙ share

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HaarPooling: Graph Pooling with Compressive Haar Basis

Deep Graph Neural Networks (GNNs) are instrumental in graph classificati...
09/25/2019 ∙ by Yu Guang Wang, et al. ∙ 13 ∙ share

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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...
10/11/2019 ∙ by Hongxu Yin, et al. ∙ 13 ∙ share

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Nonparametric Deconvolution Models

We describe nonparametric deconvolution models (NDMs), a family of Bayes...
03/17/2020 ∙ by Allison J. B. Chaney, et al. ∙ 13 ∙ share

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

Generative adversarial network (GAN) has attracted increasing attention ...
07/04/2020 ∙ by Chuan Ma, et al. ∙ 13 ∙ share

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A Mathematical Model for Linguistic Universals

Inspired by chemical kinetics and neurobiology, we propose a mathematica...
07/29/2019 ∙ by Weinan E, et al. ∙ 12 ∙ share

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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...
08/29/2019 ∙ by Aaron Sidford, et al. ∙ 12 ∙ share

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Machine learning based non-Newtonian fluid model with molecular fidelity

We introduce a machine-learning-based framework for constructing continu...
03/07/2020 ∙ by Huan Lei, et al. ∙ 12 ∙ share

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

The ability to perform effective planning is crucial for building an ins...
07/11/2020 ∙ by Zhiwei Deng, et al. ∙ 11 ∙ share

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D3D: Distilled 3D Networks for Video Action Recognition

State-of-the-art methods for video action recognition commonly use an en...
12/19/2018 ∙ by Jonathan C. Stroud, et al. ∙ 10 ∙ share

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Annealing for Distributed Global Optimization

The paper proves convergence to global optima for a class of distributed...
03/18/2019 ∙ by Brian Swenson, et al. ∙ 10 ∙ share

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Bio-Inspired Hashing for Unsupervised Similarity Search

The fruit fly Drosophila's olfactory circuit has inspired a new locality...
01/14/2020 ∙ by Chaitanya K. Ryali, et al. ∙ 10 ∙ share

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Path Integral Based Convolution and Pooling for Graph Neural Networks

Graph neural networks (GNNs) extends the functionality of traditional ne...
06/29/2020 ∙ by Zheng Ma, et al. ∙ 10 ∙ share

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Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks

Latent dynamics discovery is challenging in extracting complex dynamics ...
07/01/2019 ∙ by Qi She, et al. ∙ 9 ∙ share

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Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy

Computer vision technology is being used by many but remains representat...
12/16/2019 ∙ by Kaiyu Yang, et al. ∙ 9 ∙ share

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LagNetViP: A Lagrangian Neural Network for Video Prediction

The dominant paradigms for video prediction rely on opaque transition mo...
10/24/2020 ∙ by Christine Allen-Blanchette, et al. ∙ 9 ∙ share

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A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network

Deep neural network is an effective choice to automatically recognize hu...
01/03/2021 ∙ by Tanvir Mahmud, et al. ∙ 9 ∙ share

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Online Control with Adversarial Disturbances

We study the control of a linear dynamical system with adversarial distu...
02/23/2019 ∙ by Naman Agarwal, et al. ∙ 8 ∙ share

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On the Utility of Learning about Humans for Human-AI Coordination

While we would like agents that can coordinate with humans, current algo...
10/13/2019 ∙ by Micah Carroll, et al. ∙ 8 ∙ share

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SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models

Standard variational lower bounds used to train latent variable models p...
04/01/2020 ∙ by Yucen Luo, et al. ∙ 8 ∙ share

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Distributed Learning: Sequential Decision Making in Resource-Constrained Environments

We study cost-effective communication strategies that can be used to imp...
04/13/2020 ∙ by Udari Madhushani, et al. ∙ 8 ∙ share

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Inherent Noise in Gradient Based Methods

Previous work has examined the ability of larger capacity neural network...
05/26/2020 ∙ by Arushi Gupta, et al. ∙ 8 ∙ share

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Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates

We demonstrate an algorithm for learning a flexible color-magnitude diag...
08/21/2019 ∙ by Miles D. Cranmer, et al. ∙ 7 ∙ share

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The Efficiency of Human Cognition Reflects Planned Information Processing

Planning is useful. It lets people take actions that have desirable long...
02/13/2020 ∙ by Mark K Ho, et al. ∙ 7 ∙ share

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CornerNet: Detecting Objects as Paired Keypoints

We propose CornerNet, a new approach to object detection where we detect...
08/03/2018 ∙ by Hei Law, et al. ∙ 6 ∙ share

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On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization

Adaptive gradient methods are workhorses in deep learning. However, the ...
08/16/2018 ∙ by Dongruo Zhou, et al. ∙ 6 ∙ share

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Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks

Bayesian neural networks (BNNs) hold great promise as a flexible and pri...
10/09/2018 ∙ by Anqi Wu, et al. ∙ 6 ∙ share

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Steerable ePCA

In photon-limited imaging, the pixel intensities are affected by photon ...
12/20/2018 ∙ by Zhizhen Zhao, et al. ∙ 6 ∙ share

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