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Adam Paszke
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Automap: Towards Ergonomic Automated Parallelism for ML Models


Dec 06, 2021
Michael Schaarschmidt, Dominik Grewe, Dimitrios Vytiniotis, Adam Paszke, Georg Stefan Schmid, Tamara Norman, James Molloy, Jonathan Godwin, Norman Alexander Rink, Vinod Nair, Dan Belov

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* Workshop on ML for Systems at NeurIPS 2021 
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Memory-efficient array redistribution through portable collective communication


Dec 02, 2021
Norman A. Rink, Adam Paszke, Dimitrios Vytiniotis, Georg Stefan Schmid

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Decomposing reverse-mode automatic differentiation


May 20, 2021
Roy Frostig, Matthew J. Johnson, Dougal Maclaurin, Adam Paszke, Alexey Radul

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* Presented at the LAFI 2021 workshop at POPL, 17 January 2021 
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Tensors Fitting Perfectly


Feb 26, 2021
Adam Paszke, Brennan Saeta

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PyTorch Distributed: Experiences on Accelerating Data Parallel Training


Jun 28, 2020
Shen Li, Yanli Zhao, Rohan Varma, Omkar Salpekar, Pieter Noordhuis, Teng Li, Adam Paszke, Jeff Smith, Brian Vaughan, Pritam Damania, Soumith Chintala

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* To appear in VLDB 2020 
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PyTorch: An Imperative Style, High-Performance Deep Learning Library


Dec 03, 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

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* 12 pages, 3 figures, NeurIPS 2019 
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An Analysis of Deep Neural Network Models for Practical Applications


Apr 14, 2017
Alfredo Canziani, Adam Paszke, Eugenio Culurciello

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* 7 pages, 10 figures, legend for Figure 2 got lost :/ 
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ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation


Jun 07, 2016
Adam Paszke, Abhishek Chaurasia, Sangpil Kim, Eugenio Culurciello

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