Search Results for author: Soumith Chintala

Found 23 papers, 16 papers with code

droidlet: modular, heterogenous, multi-modal agents

1 code implementation25 Jan 2021 Anurag Pratik, Soumith Chintala, Kavya Srinet, Dhiraj Gandhi, Rebecca Qian, Yuxuan Sun, Ryan Drew, Sara Elkafrawy, Anoushka Tiwari, Tucker Hart, Mary Williamson, Abhinav Gupta, Arthur Szlam

In recent years, there have been significant advances in building end-to-end Machine Learning (ML) systems that learn at scale.

PyTorch Distributed: Experiences on Accelerating Data Parallel Training

1 code implementation28 Jun 2020 Shen Li, Yanli Zhao, Rohan Varma, Omkar Salpekar, Pieter Noordhuis, Teng Li, Adam Paszke, Jeff Smith, Brian Vaughan, Pritam Damania, Soumith Chintala

This paper presents the design, implementation, and evaluation of the PyTorch distributed data parallel module.

Generalized Inner Loop Meta-Learning

3 code implementations3 Oct 2019 Edward Grefenstette, Brandon Amos, Denis Yarats, Phu Mon Htut, Artem Molchanov, Franziska Meier, Douwe Kiela, Kyunghyun Cho, Soumith Chintala

Many (but not all) approaches self-qualifying as "meta-learning" in deep learning and reinforcement learning fit a common pattern of approximating the solution to a nested optimization problem.

Meta-Learning

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

Wasserstein Generative Adversarial Networks

no code implementations ICML 2017 Martin Arjovsky, Soumith Chintala, Léon Bottou

We introduce a new algorithm named WGAN, an alternative to traditional GAN training.

Training Language Models Using Target-Propagation

1 code implementation15 Feb 2017 Sam Wiseman, Sumit Chopra, Marc'Aurelio Ranzato, Arthur Szlam, Ruoyu Sun, Soumith Chintala, Nicolas Vasilache

While Truncated Back-Propagation through Time (BPTT) is the most popular approach to training Recurrent Neural Networks (RNNs), it suffers from being inherently sequential (making parallelization difficult) and from truncating gradient flow between distant time-steps.

Transformation-Based Models of Video Sequences

no code implementations29 Jan 2017 Joost van Amersfoort, Anitha Kannan, Marc'Aurelio Ranzato, Arthur Szlam, Du Tran, Soumith Chintala

In this work we propose a simple unsupervised approach for next frame prediction in video.

Wasserstein GAN

95 code implementations26 Jan 2017 Martin Arjovsky, Soumith Chintala, Léon Bottou

We introduce a new algorithm named WGAN, an alternative to traditional GAN training.

Image Generation Synthetic Data Generation

Semantic Segmentation using Adversarial Networks

1 code implementation25 Nov 2016 Pauline Luc, Camille Couprie, Soumith Chintala, Jakob Verbeek

Adversarial training has been shown to produce state of the art results for generative image modeling.

Semantic Segmentation

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

2 code implementations1 Nov 2016 Gabriel Synnaeve, Nantas Nardelli, Alex Auvolat, Soumith Chintala, Timothée Lacroix, Zeming Lin, Florian Richoux, Nicolas Usunier

We present TorchCraft, a library that enables deep learning research on Real-Time Strategy (RTS) games such as StarCraft: Brood War, by making it easier to control these games from a machine learning framework, here Torch.

Starcraft

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

no code implementations10 Sep 2016 Nicolas Usunier, Gabriel Synnaeve, Zeming Lin, Soumith Chintala

We consider scenarios from the real-time strategy game StarCraft as new benchmarks for reinforcement learning algorithms.

Q-Learning Starcraft

Discovering Causal Signals in Images

1 code implementation CVPR 2017 David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Schölkopf, Léon Bottou

Our experiments demonstrate the existence of a relation between the direction of causality and the difference between objects and their contexts, and by the same token, the existence of observable signals that reveal the causal dispositions of objects.

Causal Discovery

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

MazeBase: A Sandbox for Learning from Games

2 code implementations23 Nov 2015 Sainbayar Sukhbaatar, Arthur Szlam, Gabriel Synnaeve, Soumith Chintala, Rob Fergus

This paper introduces MazeBase: an environment for simple 2D games, designed as a sandbox for machine learning approaches to reasoning and planning.

Starcraft

Convolutional networks and learning invariant to homogeneous multiplicative scalings

no code implementations26 Jun 2015 Mark Tygert, Arthur Szlam, Soumith Chintala, Marc'Aurelio Ranzato, Yuandong Tian, Wojciech Zaremba

The conventional classification schemes -- notably multinomial logistic regression -- used in conjunction with convolutional networks (convnets) are classical in statistics, designed without consideration for the usual coupling with convnets, stochastic gradient descent, and backpropagation.

Classification General Classification

Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks

1 code implementation18 Jun 2015 Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus

In this paper we introduce a generative parametric model capable of producing high quality samples of natural images.

A mathematical motivation for complex-valued convolutional networks

no code implementations11 Mar 2015 Joan Bruna, Soumith Chintala, Yann Lecun, Serkan Piantino, Arthur Szlam, Mark Tygert

Courtesy of the exact correspondence, the remarkably rich and rigorous body of mathematical analysis for wavelets applies directly to (complex-valued) convnets.

Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

2 code implementations24 Dec 2014 Nicolas Vasilache, Jeff Johnson, Michael Mathieu, Soumith Chintala, Serkan Piantino, Yann Lecun

We examine the performance profile of Convolutional Neural Network training on the current generation of NVIDIA Graphics Processing Units.

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