S
Soumith Chintala
Researcher at Facebook
Publications - 47
Citations - 58853
Soumith Chintala is an academic researcher from Facebook. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 29, co-authored 43 publications receiving 41035 citations. Previous affiliations of Soumith Chintala include VIT University & Courant Institute of Mathematical Sciences.
Papers
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Automatic differentiation in PyTorch
Adam Paszke,Sam Gross,Soumith Chintala,Gregory Chanan,Edward Z. Yang,Zachary DeVito,Zeming Lin,Alban Desmaison,Luca Antiga,Adam Lerer +9 more
TL;DR: An automatic differentiation module of PyTorch is described — a library designed to enable rapid research on machine learning models that focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead.
Posted Content
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke,Sam Gross,Francisco Massa,Adam Lerer,James Bradbury,Gregory Chanan,Trevor Killeen,Zeming Lin,Natalia Gimelshein,Luca Antiga,Alban Desmaison,Andreas Kopf,Edward Z. Yang,Zachary DeVito,Martin Raison,Alykhan Tejani,Sasank Chilamkurthy,Benoit Steiner,Lu Fang,Junjie Bai,Soumith Chintala +20 more
TL;DR: PyTorch as discussed by the authors is a machine learning library that provides an imperative and Pythonic programming style that makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs.
Proceedings Article
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke,Sam Gross,Francisco Massa,Adam Lerer,James Bradbury,Gregory Chanan,Trevor Killeen,Zeming Lin,Natalia Gimelshein,Luca Antiga,Alban Desmaison,Andreas Kopf,Edward Z. Yang,Zachary DeVito,Martin Raison,Alykhan Tejani,Sasank Chilamkurthy,Benoit Steiner,Lu Fang,Junjie Bai,Soumith Chintala +20 more
TL;DR: This paper details the principles that drove the implementation of PyTorch and how they are reflected in its architecture, and explains how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance.
Posted Content
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
TL;DR: This work introduces a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrates that they are a strong candidate for unsupervised learning.
Proceedings Article
Wasserstein Generative Adversarial Networks
TL;DR: This work introduces a new algorithm named WGAN, an alternative to traditional GAN training that can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches.