activity
20162020
most citedPyTorch: An Imperative Style, High-Performance Deep Learning Library

16.2k citations · 16.6k across the 8 of their papers we have counts for

collaborators

9 papers

cs.CL2020★ 18 cited

Residual Energy-Based Models for Text

Anton Bakhtin, Yuntian Deng, Sam Gross +3

Current large-scale auto-regressive language models display impressive fluency and can generate convincing text. In this work we start by asking the question: Can the generations o…

cs.LG2019★ 16.2k cited

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Adam Paszke, Sam Gross, Francisco Massa +18

Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible…

cs.LG2019★ 30 cited

Real or Fake? Learning to Discriminate Machine from Human Generated Text

Anton Bakhtin, Sam Gross, Myle Ott +3

Energy-based models (EBMs), a.k.a. un-normalized models, have had recent successes in continuous spaces. However, they have not been successfully applied to model text sequences. W…

cs.CL2019★ 163 cited

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

Myle Ott, Sergey Edunov, Alexei Baevski +5

fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text…

cs.AI2018★ 21 cited

Deep Counterfactual Regret Minimization

Noam Brown, Adam Lerer, Sam Gross +1

Counterfactual Regret Minimization (CFR) is the leading framework for solving large imperfect-information games. It converges to an equilibrium by iteratively traversing the game t…

cs.CV2017★ 7 cited

Hard Mixtures of Experts for Large Scale Weakly Supervised Vision

Sam Gross, Marc'Aurelio Ranzato, Arthur Szlam

Training convolutional networks (CNN's) that fit on a single GPU with minibatch stochastic gradient descent has become effective in practice. However, there is still no effective m…

cs.CV2016★ 130 cited

Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

Remi Denton, Sam Gross, Rob Fergus

We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss. Images with random patches removed are presented to a generator w…

cs.CV2016

A MultiPath Network for Object Detection

Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin +4

The recent COCO object detection dataset presents several new challenges for object detection. In particular, it contains objects at a broad range of scales, less prototypical imag…

cs.AI2016★ 75 cited

Learning Physical Intuition of Block Towers by Example

Adam Lerer, Sam Gross, Rob Fergus

Wooden blocks are a common toy for infants, allowing them to develop motor skills and gain intuition about the physical behavior of the world. In this paper, we explore the ability…