Search Results for author: Sam Gross

Found 10 papers, 7 papers with code

Residual Energy-Based Models for Text

no code implementations6 Apr 2020 Anton Bakhtin, Yuntian Deng, Sam Gross, Myle Ott, Marc'Aurelio Ranzato, Arthur Szlam

Current large-scale auto-regressive language models display impressive fluency and can generate convincing text.

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

5 code implementations NAACL 2019 Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli

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 generation tasks.

Language Modelling Text Generation +1

Deep Counterfactual Regret Minimization

5 code implementations1 Nov 2018 Noam Brown, Adam Lerer, Sam Gross, Tuomas Sandholm

This paper introduces Deep Counterfactual Regret Minimization, a form of CFR that obviates the need for abstraction by instead using deep neural networks to approximate the behavior of CFR in the full game.

Automatic Differentiation in PyTorch

1 code implementation NIPS 2017 2017 Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, Adam Lerer

In this article, we describe an automatic differentiation module of PyTorch — a library designed to enable rapid research on machine learning models.

Dimensionality Reduction General Classification

Hard Mixtures of Experts for Large Scale Weakly Supervised Vision

no code implementations CVPR 2017 Sam Gross, Marc'Aurelio Ranzato, Arthur Szlam

In this work we show that a simple hard mixture of experts model can be efficiently trained to good effect on large scale hashtag (multilabel) prediction tasks.

A MultiPath Network for Object Detection

1 code implementation7 Apr 2016 Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin, Pedro O. Pinheiro, Sam Gross, Soumith Chintala, Piotr Dollár

To address these challenges, we test three modifications to the standard Fast R-CNN object detector: (1) skip connections that give the detector access to features at multiple network layers, (2) a foveal structure to exploit object context at multiple object resolutions, and (3) an integral loss function and corresponding network adjustment that improve localization.

Instance Segmentation Object Detection

Learning Physical Intuition of Block Towers by Example

2 code implementations3 Mar 2016 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.

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