Brown University

∙ Providence , United States ∙ 0 followers

Brown University is a private Ivy League research university in Providence, Rhode Island, United States.

Streamlining Tensor and Network Pruning in PyTorch

In order to contrast the explosion in size of state-of-the-art machine l...
04/28/2020 ∙ by Michela Paganini, et al. ∙ 47 ∙ share

read it
DeepXDE: A deep learning library for solving differential equations

Deep learning has achieved remarkable success in diverse applications; h...
07/10/2019 ∙ by Lu Lu, et al. ∙ 33 ∙ share

read it
Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness

The accuracy of deep learning, i.e., deep neural networks, can be charac...
05/27/2019 ∙ by Pengzhan Jin, et al. ∙ 31 ∙ share

read it
15 Keypoints Is All You Need

Pose tracking is an important problem that requires identifying unique h...
12/05/2019 ∙ by Michael Snower, et al. ∙ 28 ∙ share

read it
Unsupervised Deep Metric Learning via Auxiliary Rotation Loss

Deep metric learning is an important area due to its applicability to ma...
11/16/2019 ∙ by Xuefei Cao, et al. ∙ 26 ∙ share

read it
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning

Human social behavior is structured by relationships. We form teams, gro...
01/18/2019 ∙ by Michael Shum, et al. ∙ 20 ∙ share

read it
Successor Features Support Model-based and Model-free Reinforcement Learning

One key challenge in reinforcement learning is the ability to generalize...
01/31/2019 ∙ by Lucas Lehnert, et al. ∙ 14 ∙ share

read it
Individual predictions matter: Assessing the effect of data ordering in training fine-tuned CNNs for medical imaging

We reproduced the results of CheXNet with fixed hyperparameters and 50 d...
12/08/2019 ∙ by John R. Zech, et al. ∙ 12 ∙ share

read it
Teaching with IMPACT

Like many problems in AI in their general form, supervised learning is c...
03/14/2019 ∙ by Carl Trimbach, et al. ∙ 10 ∙ share

read it
Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-informed Neural Networks

The Fokker-Planck (FP) equation governing the evolution of the probabili...
08/24/2020 ∙ by Xiaoli Chen, et al. ∙ 9 ∙ share

read it
PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs

Physics-informed neural networks (PINNs) encode physical conservation la...
09/23/2019 ∙ by Xuhui Meng, et al. ∙ 8 ∙ share

read it
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

read it
Wasserstein Adversarial Autoencoders for Knowledge Graph Embedding based Drug-Drug Interaction Prediction

Interaction between pharmacological agents can trigger unexpected advers...
04/15/2020 ∙ by Yuanfei Dai, et al. ∙ 7 ∙ share

read it
Robot Object Retrieval with Contextual Natural Language Queries

Natural language object retrieval is a highly useful yet challenging tas...
06/23/2020 ∙ by Thao Nguyen, et al. ∙ 7 ∙ share

read it
Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks

One of the open problems in scientific computing is the long-time integr...
05/03/2019 ∙ by Dongkun Zhang, et al. ∙ 6 ∙ share

read it
Stackelberg Punishment and Bully-Proofing Autonomous Vehicles

Mutually beneficial behavior in repeated games can be enforced via the t...
08/23/2019 ∙ by Matt Cooper, et al. ∙ 6 ∙ share

read it
Interactive Learning of Environment Dynamics for Sequential Tasks

In order for robots and other artificial agents to efficiently learn to ...
07/19/2019 ∙ by Robert Loftin, et al. ∙ 5 ∙ share

read it
Finding Options that Minimize Planning Time

While adding temporally abstract actions, or options, to an agent's acti...
10/16/2018 ∙ by Yuu Jinnai, et al. ∙ 4 ∙ share

read it
Trainability and Data-dependent Initialization of Over-parameterized ReLU Neural Networks

A neural network is said to be over-specified if its representational po...
07/23/2019 ∙ by Yeonjong Shin, et al. ∙ 3 ∙ share

read it
Potential Flow Generator with L_2 Optimal Transport Regularity for Generative Models

We propose a potential flow generator with L_2 optimal transport regular...
08/29/2019 ∙ by Liu Yang, et al. ∙ 2 ∙ share

read it
Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding

We introduce Room-Across-Room (RxR), a new Vision-and-Language Navigatio...
10/15/2020 ∙ by Alexander Ku, et al. ∙ 2 ∙ share

read it
A survey of statistical learning techniques as applied to inexpensive pediatric Obstructive Sleep Apnea data

Pediatric obstructive sleep apnea affects an estimated 1-5 elementary-sc...
02/17/2020 ∙ by Emily T. Winn, et al. ∙ 1 ∙ share

read it
Automatic Discovery and Optimization of Parts for Image Classification

Part-based representations have been shown to be very useful for image c...
12/20/2014 ∙ by Sobhan Naderi Parizi, et al. ∙ 0 ∙ share

read it
Joint and conditional estimation of tagging and parsing models

This paper compares two different ways of estimating statistical languag...
05/07/2001 ∙ by Mark Johnson, et al. ∙ 0 ∙ share

read it
Explaining away ambiguity: Learning verb selectional preference with Bayesian networks

This paper presents a Bayesian model for unsupervised learning of verb s...
08/22/2000 ∙ by Massimiliano Ciaramita, et al. ∙ 0 ∙ share

read it
Learning Robust Dialog Policies in Noisy Environments

Modern virtual personal assistants provide a convenient interface for co...
12/11/2017 ∙ by Maryam Fazel-Zarandi, et al. ∙ 0 ∙ share

read it
Implementing the Deep Q-Network

The Deep Q-Network proposed by Mnih et al. [2015] has become a benchmark...
11/20/2017 ∙ by Melrose Roderick, et al. ∙ 0 ∙ share

read it
An Improved Training Procedure for Neural Autoregressive Data Completion

Neural autoregressive models are explicit density estimators that achiev...
11/23/2017 ∙ by Maxime Voisin, et al. ∙ 0 ∙ share

read it
Deep Abstract Q-Networks

We examine the problem of learning and planning on high-dimensional doma...
10/02/2017 ∙ by Melrose Roderick, et al. ∙ 0 ∙ share

read it
Hardware-Software Codesign of Accurate, Multiplier-free Deep Neural Networks

While Deep Neural Networks (DNNs) push the state-of-the-art in many mach...
05/11/2017 ∙ by Hokchhay Tann, et al. ∙ 0 ∙ share

read it
Summable Reparameterizations of Wasserstein Critics in the One-Dimensional Setting

Generative adversarial networks (GANs) are an exciting alternative to al...
09/19/2017 ∙ by Christopher Grimm, et al. ∙ 0 ∙ share

read it
Active Exploration for Learning Symbolic Representations

We introduce an online active exploration algorithm for data-efficiently...
09/05/2017 ∙ by Garrett Andersen, et al. ∙ 0 ∙ share

read it
Mean Actor Critic

We propose a new algorithm, Mean Actor-Critic (MAC), for discrete-action...
09/01/2017 ∙ by Kavosh Asadi, et al. ∙ 0 ∙ share

read it
Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks

Deep neural networks are gaining in popularity as they are used to gener...
12/12/2016 ∙ by Soheil Hashemi, et al. ∙ 0 ∙ share

read it
Learning to Infer Graphics Programs from Hand-Drawn Images

We introduce a model that learns to convert simple hand drawings into gr...
07/30/2017 ∙ by Kevin Ellis, et al. ∙ 0 ∙ share

read it
Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off

We present a novel dynamic configuration technique for deep neural netwo...
07/19/2016 ∙ by Hokchhay Tann, et al. ∙ 0 ∙ share

read it
Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processes

We introduce a new formulation of the Hidden Parameter Markov Decision P...
06/20/2017 ∙ by Taylor Killian, et al. ∙ 0 ∙ share

read it
Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning

End-to-end learning of recurrent neural networks (RNNs) is an attractive...
02/10/2017 ∙ by Jason D. Williams, et al. ∙ 0 ∙ share

read it
Near Optimal Behavior via Approximate State Abstraction

The combinatorial explosion that plagues planning and reinforcement lear...
01/15/2017 ∙ by David Abel, et al. ∙ 0 ∙ share

read it
Agent-Agnostic Human-in-the-Loop Reinforcement Learning

Providing Reinforcement Learning agents with expert advice can dramatica...
01/15/2017 ∙ by David Abel, et al. ∙ 0 ∙ share

read it
Sample-efficient Deep Reinforcement Learning for Dialog Control

Representing a dialog policy as a recurrent neural network (RNN) is attr...
12/18/2016 ∙ by Kavosh Asadi, et al. ∙ 0 ∙ share

read it
The Surfacing of Multiview 3D Drawings via Lofting and Occlusion Reasoning

The three-dimensional reconstruction of scenes from multiple views has m...
07/13/2017 ∙ by Anil Usumezbas, et al. ∙ 0 ∙ share

read it
Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes

Due to physiological variation, patients diagnosed with the same conditi...
12/01/2016 ∙ by Taylor Killian, et al. ∙ 0 ∙ share

read it
Parametric Gaussian Process Regression for Big Data

This work introduces the concept of parametric Gaussian processes (PGPs)...
04/11/2017 ∙ by Maziar Raissi, et al. ∙ 0 ∙ share

read it
Fast Learning of Clusters and Topics via Sparse Posteriors

Mixture models and topic models generate each observation from a single ...
09/23/2016 ∙ by Michael C. Hughes, et al. ∙ 0 ∙ share

read it
Critical Contours: An Invariant Linking Image Flow with Salient Surface Organization

We exploit a key result from visual psychophysics -- that individuals pe...
05/20/2017 ∙ by Benjamin S. Kunsberg, et al. ∙ 0 ∙ share

read it
Embedding Projector: Interactive Visualization and Interpretation of Embeddings

Embeddings are ubiquitous in machine learning, appearing in recommender ...
11/16/2016 ∙ by Daniel Smilkov, et al. ∙ 0 ∙ share

read it
Covariance Plasticity and Regulated Criticality

We propose that a regulation mechanism based on Hebbian covariance plast...
02/20/2002 ∙ by Elie Bienenstock, et al. ∙ 0 ∙ share

read it
The discriminative Kalman filter for nonlinear and non-Gaussian sequential Bayesian filtering

The Kalman filter (KF) is used in a variety of applications for computin...
08/23/2016 ∙ by Michael C. Burkhart, et al. ∙ 0 ∙ share

read it
Column Generation for Interaction Coverage in Combinatorial Software Testing

This paper proposes a novel column generation framework for combinatoria...
12/19/2017 ∙ by Serdar Kadioglu, et al. ∙ 0 ∙ share

read it

Brown University

Featured Researchers