b'Rob Fergus'

research

∙ 09/20/2023

Hierarchical reinforcement learning with natural language subgoals

Hierarchical reinforcement learning has been a compelling approach for a...

0 Arun Ahuja, et al. ∙

research

∙ 05/30/2023

NetHack is Hard to Hack

Neural policy learning methods have achieved remarkable results in vario...

0 Ulyana Piterbarg, et al. ∙

research

∙ 03/31/2023

Accelerating exploration and representation learning with offline pre-training

Sequential decision-making agents struggle with long horizon tasks, sinc...

0 Bogdan Mazoure, et al. ∙

research

∙ 02/22/2023

Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC

Since their introduction, diffusion models have quickly become the preva...

0 Yilun Du, et al. ∙

research

∙ 02/01/2023

Collaborating with language models for embodied reasoning

Reasoning in a complex and ambiguous environment is a key goal for Reinf...

0 Ishita Dasgupta, et al. ∙

research

∙ 01/29/2023

Distilling Internet-Scale Vision-Language Models into Embodied Agents

Instruction-following agents must ground language into their observation...

0 Theodore Sumers, et al. ∙

research

∙ 01/27/2023

EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval

Large neural models (such as Transformers) achieve state-of-the-art perf...

12 Seungyeon Kim, et al. ∙

research

∙ 10/31/2022

Learning to Navigate Wikipedia by Taking Random Walks

A fundamental ability of an intelligent web-based agent is seeking out a...

0 Manzil Zaheer, et al. ∙

research

∙ 07/20/2021

Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

We present DrQ-v2, a model-free reinforcement learning (RL) algorithm fo...

15 Denis Yarats, et al. ∙

research

∙ 07/08/2021

Imitation by Predicting Observations

Imitation learning enables agents to reuse and adapt the hard-won expert...

0 Andrew Jaegle, et al. ∙

research

∙ 03/14/2021

Offline Reinforcement Learning with Fisher Divergence Critic Regularization

Many modern approaches to offline Reinforcement Learning (RL) utilize be...

0 Ilya Kostrikov, et al. ∙

research

∙ 02/22/2021

Reinforcement Learning with Prototypical Representations

Learning effective representations in image-based environments is crucia...

21 Denis Yarats, et al. ∙

research

∙ 02/20/2021

Decoupling Value and Policy for Generalization in Reinforcement Learning

Standard deep reinforcement learning algorithms use a shared representat...

0 Roberta Raileanu, et al. ∙

research

∙ 07/06/2020

Fast Adaptation via Policy-Dynamics Value Functions

Standard RL algorithms assume fixed environment dynamics and require a s...

12 Roberta Raileanu, et al. ∙

research

∙ 06/23/2020

Automatic Data Augmentation for Generalization in Deep Reinforcement Learning

Deep reinforcement learning (RL) agents often fail to generalize to unse...

0 Roberta Raileanu, et al. ∙

research

∙ 04/28/2020

Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels

We propose a simple data augmentation technique that can be applied to s...

10 Ilya Kostrikov, et al. ∙

research

∙ 04/27/2020

Energy-based models for atomic-resolution protein conformations

We propose an energy-based model (EBM) of protein conformations that ope...

9 Yilun Du, et al. ∙

research

∙ 10/02/2019

Improving Sample Efficiency in Model-Free Reinforcement Learning from Images

Training an agent to solve control tasks directly from high-dimensional ...

18 Denis Yarats, et al. ∙

research

∙ 09/12/2019

Finding Generalizable Evidence by Learning to Convince Q A Models

We propose a system that finds the strongest supporting evidence for a g...

15 Ethan Perez, et al. ∙

research

∙ 01/17/2019

Disentangling Video with Independent Prediction

We propose an unsupervised variational model for disentangling video int...

0 William F. Whitney, et al. ∙

research

∙ 11/22/2018

Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning

In hierarchical reinforcement learning a major challenge is determining ...

0 Sainbayar Sukhbaatar, et al. ∙

research

∙ 03/20/2018

IntPhys: A Framework and Benchmark for Visual Intuitive Physics Reasoning

In order to reach human performance on complex visual tasks, artificial ...

0 Ronan Riochet, et al. ∙

research

∙ 03/01/2018

Composable Planning with Attributes

The tasks that an agent will need to solve often are not known during tr...

0 Amy Zhang, et al. ∙

research

∙ 02/26/2018

Modeling Others using Oneself in Multi-Agent Reinforcement Learning

We consider the multi-agent reinforcement learning setting with imperfec...

0 Roberta Raileanu, et al. ∙

research

∙ 02/21/2018

Stochastic Video Generation with a Learned Prior

Generating video frames that accurately predict future world states is c...

0 Emily Denton, et al. ∙

research

∙ 12/04/2017

Learning by Asking Questions

We introduce an interactive learning framework for the development and t...

0 Ishan Misra, et al. ∙

research

∙ 11/19/2016

Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

We introduce a simple semi-supervised learning approach for images based...

0 Emily Denton, et al. ∙

research

∙ 03/03/2016

Learning Physical Intuition of Block Towers by Example

Wooden blocks are a common toy for infants, allowing them to develop mot...

0 Adam Lerer, et al. ∙

research

∙ 12/07/2015

Simple Baseline for Visual Question Answering

We describe a very simple bag-of-words baseline for visual question answ...

0 Bolei Zhou, et al. ∙

research

∙ 11/23/2015

MazeBase: A Sandbox for Learning from Games

This paper introduces MazeBase: an environment for simple 2D games, desi...

0 Sainbayar Sukhbaatar, et al. ∙

research

∙ 11/23/2015

Learning Simple Algorithms from Examples

We present an approach for learning simple algorithms such as copying, m...

0 Wojciech Zaremba, et al. ∙

research

∙ 06/18/2015

Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks

In this paper we introduce a generative parametric model capable of prod...

0 Emily Denton, et al. ∙

research

∙ 03/31/2015

End-To-End Memory Networks

We introduce a neural network with a recurrent attention model over a po...

0 Sainbayar Sukhbaatar, et al. ∙

research

∙ 01/23/2015

Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues

We explore the task of recognizing peoples' identities in photo albums i...

0 Ning Zhang, et al. ∙

research

∙ 12/02/2014

Learning Spatiotemporal Features with 3D Convolutional Networks

We propose a simple, yet effective approach for spatiotemporal feature l...

0 Du Tran, et al. ∙

research

∙ 11/19/2014

End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression

Deformable Parts Models and Convolutional Networks each have achieved no...

0 Li Wan, et al. ∙

research

∙ 11/18/2014

Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture

In this paper we address three different computer vision tasks using a s...

0 David Eigen, et al. ∙

research

∙ 07/02/2014

Deep Poselets for Human Detection

We address the problem of detecting people in natural scenes using a par...

0 Lubomir Bourdev, et al. ∙

research

∙ 06/09/2014

Depth Map Prediction from a Single Image using a Multi-Scale Deep Network

Predicting depth is an essential component in understanding the 3D geome...

0 David Eigen, et al. ∙

research

∙ 06/09/2014

Training Convolutional Networks with Noisy Labels

The availability of large labeled datasets has allowed Convolutional Net...

0 Sainbayar Sukhbaatar, et al. ∙

research

∙ 04/02/2014

Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation

We present techniques for speeding up the test-time evaluation of large ...

0 Emily Denton, et al. ∙

research

∙ 12/21/2013

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

We present an integrated framework for using Convolutional Networks for ...

0 Pierre Sermanet, et al. ∙

research

∙ 12/21/2013

Intriguing properties of neural networks

Deep neural networks are highly expressive models that have recently ach...

0 Christian Szegedy, et al. ∙

research

∙ 12/06/2013

Understanding Deep Architectures using a Recursive Convolutional Network

A key challenge in designing convolutional network models is sizing them...

0 David Eigen, et al. ∙

research

∙ 11/16/2013

Blind Deconvolution with Non-local Sparsity Reweighting

Blind deconvolution has made significant progress in the past decade. Mo...

0 Dilip Krishnan, et al. ∙

research

∙ 11/12/2013

Visualizing and Understanding Convolutional Networks

Large Convolutional Network models have recently demonstrated impressive...

0 Matthew D. Zeiler, et al. ∙

research

∙ 01/16/2013

Stochastic Pooling for Regularization of Deep Convolutional Neural Networks

We introduce a simple and effective method for regularizing large convol...

0 Matthew D. Zeiler, et al. ∙

research

∙ 06/30/2012

Differentiable Pooling for Hierarchical Feature Learning

We introduce a parametric form of pooling, based on a Gaussian, which ca...

0 Matthew D. Zeiler, et al. ∙

Rob Fergus

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