Search Results for author: Soumith Chintala

Found 30 papers, 19 papers with code

On Bringing Robots Home

1 code implementation27 Nov 2023 Nur Muhammad Mahi Shafiullah, Anant Rai, Haritheja Etukuru, Yiqian Liu, Ishan Misra, Soumith Chintala, Lerrel Pinto

We use the Stick to collect 13 hours of data in 22 homes of New York City, and train Home Pretrained Representations (HPR).

Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play

no code implementations21 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.

Representation Learning

Navigating to Objects in the Real World

no code implementations2 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.

Navigate Visual Navigation

Holo-Dex: Teaching Dexterity with Immersive Mixed Reality

no code implementations12 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.

Mixed Reality

CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory

2 code implementations11 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.

Segmentation Semantic Segmentation +1

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

3 code implementations28 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 reinforcement-learning +1

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.

Clustering Dimensionality Reduction +1

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

120 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.

Segmentation 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.

BIG-bench Machine Learning Starcraft

Discovering Causal Signals in Images

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.

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 +2

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.

Negation Reinforcement Learning (RL) +1

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 +1

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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