2017-automatic-differentiation-in-pytorch.md

title	authors	fieldsOfStudy	filesize_readable	meta_key	meta_relpath	numCitedBy	pdf_relpath	reading_status	ref_count	tags	url_slug	urls	venue	year
Automatic differentiation in PyTorch	Adam Paszke S. Gross Soumith Chintala Gregory Chanan E. Yang Zach DeVito Zeming Lin Alban Desmaison L. Antiga Adam Lerer	Computer Science	50.8 KB	2017-automatic-differentiation-in-pytorch	paper-extra-data/pdf-meta/2017-automatic-differentiation-in-pytorch.yaml	10406	paper-repo/pdfs/2017-automatic-differentiation-in-pytorch.pdf	TBD	7	gen-from-ref paper pytorch	2017-automatic-differentiation-in-pytorch	https://www.semanticscholar.org/paper/Automatic-differentiation-in-PyTorch-Paszke-Gross/b36a5bb1707bb9c70025294b3a310138aae8327a?sort=total-citations		2017

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Automatic differentiation in PyTorch

Abstract

In this article, we describe an automatic differentiation module of PyTorch — a library designed to enable rapid research on machine learning models. It builds upon a few projects, most notably Lua Torch, Chainer, and HIPS Autograd [4], and provides a high performance environment with easy access to automatic differentiation of models executed on different devices (CPU and GPU). To make prototyping easier, PyTorch does not follow the symbolic approach used in many other deep learning frameworks, but focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead. Note that this preprint is a draft of certain sections from an upcoming paper covering all PyTorch features.

Paper References

1. Modeling, Inference and Optimization With Composable Differentiable Procedures
2. DyNet - The Dynamic Neural Network Toolkit
3. TensorFlow - Large-Scale Machine Learning on Heterogeneous Distributed Systems
4. Evaluating derivatives - principles and techniques of algorithmic differentiation, Second Edition
5. Compiling fast partial derivatives of functions given by algorithms
6. SciPy - Open Source Scientific Tools for Python