no code implementations • 21 Mar 2023 • Irmak Guzey, Ben Evans, Soumith Chintala, Lerrel Pinto
In the first phase, we collect 2. 5 hours of play data, which is used to train self-supervised tactile encoders.
no code implementations • 2 Dec 2022 • Theophile Gervet, Soumith Chintala, Dhruv Batra, Jitendra Malik, Devendra Singh Chaplot
In contrast, end-to-end learning does not, dropping from 77% simulation to 23% real-world success rate due to a large image domain gap between simulation and reality.
no code implementations • 12 Oct 2022 • Sridhar Pandian Arunachalam, Irmak Güzey, Soumith Chintala, Lerrel Pinto
A fundamental challenge in teaching robots is to provide an effective interface for human teachers to demonstrate useful skills to a robot.
2 code implementations • 11 Oct 2022 • Nur Muhammad Mahi Shafiullah, Chris Paxton, Lerrel Pinto, Soumith Chintala, Arthur Szlam
We propose CLIP-Fields, an implicit scene model that can be used for a variety of tasks, such as segmentation, instance identification, semantic search over space, and view localization.
2 code implementations • 28 Oct 2021 • Yao-Yuan Yang, Moto Hira, Zhaoheng Ni, Anjali Chourdia, Artyom Astafurov, Caroline Chen, Ching-Feng Yeh, Christian Puhrsch, David Pollack, Dmitriy Genzel, Donny Greenberg, Edward Z. Yang, Jason Lian, Jay Mahadeokar, Jeff Hwang, Ji Chen, Peter Goldsborough, Prabhat Roy, Sean Narenthiran, Shinji Watanabe, Soumith Chintala, Vincent Quenneville-Bélair, Yangyang Shi
This document describes version 0. 10 of TorchAudio: building blocks for machine learning applications in the audio and speech processing domain.
1 code implementation • 25 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.
2 code implementations • 28 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.
2 code implementations • NeurIPS 2019 • Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala
Deep learning frameworks have often focused on either usability or speed, but not both.
3 code implementations • 3 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.
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.
no code implementations • ICML 2017 • Martin Arjovsky, Soumith Chintala, Léon Bottou
We introduce a new algorithm named WGAN, an alternative to traditional GAN training.
1 code implementation • 15 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.
no code implementations • 29 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.
115 code implementations • 26 Jan 2017 • Martin Arjovsky, Soumith Chintala, Léon Bottou
We introduce a new algorithm named WGAN, an alternative to traditional GAN training.
1 code implementation • 25 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.
2 code implementations • 1 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.
no code implementations • 10 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.
2 code implementations • 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.
1 code implementation • 7 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.
Ranked #101 on Instance Segmentation on COCO test-dev
no code implementations • NeurIPS 2015 • Emily L. Denton, Soumith Chintala, Arthur Szlam, Rob Fergus
In this paper we introduce a generative model capable of producing high quality samples of natural images.
2 code implementations • 23 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.
259 code implementations • 19 Nov 2015 • Alec Radford, Luke Metz, Soumith Chintala
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications.
Ranked #9 on Image Clustering on Tiny-ImageNet
no code implementations • 26 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.
1 code implementation • 18 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.
no code implementations • 11 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.
2 code implementations • 24 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.
no code implementations • CVPR 2013 • Pierre Sermanet, Koray Kavukcuoglu, Soumith Chintala, Yann Lecun
Pedestrian detection is a problem of considerable practical interest.
2 code implementations • 18 Apr 2012 • Pierre Sermanet, Soumith Chintala, Yann Lecun
We classify digits of real-world house numbers using convolutional neural networks (ConvNets).