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

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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

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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

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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

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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

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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

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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

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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

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Learning Poisson systems and trajectories of autonomous systems via Poisson neural networks

We propose the Poisson neural networks (PNNs) to learn Poisson systems a...
12/05/2020 ∙ by Pengzhan Jin, et al. ∙ 12 ∙ share

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A Caputo fractional derivative-based algorithm for optimization

We propose a novel Caputo fractional derivative-based optimization algor...
04/06/2021 ∙ by Yeonjong Shin, et al. ∙ 11 ∙ share

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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

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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

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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

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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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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

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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

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Recurrent neural circuits for contour detection

We introduce a deep recurrent neural network architecture that approxima...
10/29/2020 ∙ by Drew Linsley, et al. ∙ 7 ∙ share

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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

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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

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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

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Bootstrapping Motor Skill Learning with Motion Planning

Learning a robot motor skill from scratch is impractically slow; so much...
01/12/2021 ∙ by Ben Abbatematteo, et al. ∙ 5 ∙ share

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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