Soumith Chintala

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Artificial Intelligence Research Engineer at Facebook AI Research (FAIR) since 2014, Software Engineer at Museami from 2012-2014, Junior Research Scientist at New York University from 2011-2013, Research Intern at Siemens 2011, Client Support at New York University from 2010-2011, Research Scholar at Carnegie Mellon University from 2009-2010.

Featured Co-authors

Bernhard Schölkopf
176 publications
Kyunghyun Cho
172 publications
Yann LeCun
84 publications
Joan Bruna
75 publications
Arthur Szlam
57 publications
Douwe Kiela
56 publications
Gabriel Synnaeve
50 publications
Marc'Aurelio Ranzato
43 publications
Yuandong Tian
43 publications
Rob Fergus
40 publications
Koray Kavukcuoglu
39 publications

PyTorch Distributed: Experiences on Accelerating Data Parallel Training

This paper presents the design, implementation, and evaluation of the Py...
06/28/2020 ∙ by Shen Li, et al. ∙ 0 ∙ share

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

Deep learning frameworks have often focused on either usability or speed...
12/03/2019 ∙ by Adam Paszke, et al. ∙ 90 ∙ share

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Generalized Inner Loop Meta-Learning

Many (but not all) approaches self-qualifying as "meta-learning" in deep...
10/03/2019 ∙ by Edward Grefenstette, et al. ∙ 12 ∙ share

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Training Language Models Using Target-Propagation

While Truncated Back-Propagation through Time (BPTT) is the most popular...
02/15/2017 ∙ by Sam Wiseman, et al. ∙ 0 ∙ share

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Transformation-Based Models of Video Sequences

In this work we propose a simple unsupervised approach for next frame pr...
01/29/2017 ∙ by Joost van Amersfoort, et al. ∙ 0 ∙ share

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Wasserstein GAN

We introduce a new algorithm named WGAN, an alternative to traditional G...
01/26/2017 ∙ by Martin Arjovsky, et al. ∙ 0 ∙ share

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Semantic Segmentation using Adversarial Networks

Adversarial training has been shown to produce state of the art results ...
11/25/2016 ∙ by Pauline Luc, et al. ∙ 0 ∙ share

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TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games

We present TorchCraft, a library that enables deep learning research on ...
11/01/2016 ∙ by Gabriel Synnaeve, et al. ∙ 0 ∙ share

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Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks

We consider scenarios from the real-time strategy game StarCraft as new ...
09/10/2016 ∙ by Nicolas Usunier, et al. ∙ 0 ∙ share

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Discovering Causal Signals in Images

This paper establishes the existence of observable footprints that revea...
05/26/2016 ∙ by David Lopez-Paz, et al. ∙ 0 ∙ share

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A MultiPath Network for Object Detection

The recent COCO object detection dataset presents several new challenges...
04/07/2016 ∙ by Sergey Zagoruyko, et al. ∙ 0 ∙ share

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MazeBase: A Sandbox for Learning from Games

This paper introduces MazeBase: an environment for simple 2D games, desi...
11/23/2015 ∙ by Sainbayar Sukhbaatar, et al. ∙ 0 ∙ share

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Convolutional networks and learning invariant to homogeneous multiplicative scalings

The conventional classification schemes -- notably multinomial logistic ...
06/26/2015 ∙ by Mark Tygert, et al. ∙ 0 ∙ share

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Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks

In this paper we introduce a generative parametric model capable of prod...
06/18/2015 ∙ by Emily Denton, et al. ∙ 0 ∙ share

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A mathematical motivation for complex-valued convolutional networks

A complex-valued convolutional network (convnet) implements the repeated...
03/11/2015 ∙ by Joan Bruna, et al. ∙ 0 ∙ share

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Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

We examine the performance profile of Convolutional Neural Network train...
12/24/2014 ∙ by Nicolas Vasilache, et al. ∙ 0 ∙ share

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Pedestrian Detection with Unsupervised Multi-Stage Feature Learning

Pedestrian detection is a problem of considerable practical interest. Ad...
12/01/2012 ∙ by Pierre Sermanet, et al. ∙ 0 ∙ share

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Convolutional Neural Networks Applied to House Numbers Digit Classification

We classify digits of real-world house numbers using convolutional neura...
04/18/2012 ∙ by Pierre Sermanet, et al. ∙ 0 ∙ share

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