Adam Paszke

According to our database, Adam Paszke authored at least 19 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Parallel Algebraic Effect Handlers.

Ningning Xie, Daniel D. Johnson, Dougal Maclaurin, Adam Paszke

Proc. ACM Program. Lang., 2024

RecurrentGemma: Moving Past Transformers for Efficient Open Language Models.

Aleksandar Botev, Soham De, Samuel L. Smith, Anushan Fernando, George-Cristian Muraru, Ruba Haroun, Leonard Berrada, Razvan Pascanu, Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Sertan Girgin, Olivier Bachem, Alek Andreev, Kathleen Kenealy, Thomas Mesnard, Cassidy Hardin, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Armand Joulin, Noah Fiedel, Evan Senter, Yutian Chen, Srivatsan Srinivasan, Guillaume Desjardins, David Budden, Arnaud Doucet, Sharad Vikram, Adam Paszke, Trevor Gale, Sebastian Borgeaud, Charlie Chen, Andy Brock, Antonia Paterson, Jenny Brennan, Meg Risdal, Raj Gundluru, Nesh Devanathan, Paul Mooney, Nilay Chauhan, Phil Culliton, Luiz GUStavo Martins, Elisa Bandy, David Huntsperger, Glenn Cameron, Arthur Zucker, Tris Warkentin, Ludovic Peran, Minh Giang, Zoubin Ghahramani, Clément Farabet, Koray Kavukcuoglu, Demis Hassabis, Raia Hadsell, Yee Whye Teh, Nando de Frietas

CoRR, 2024

PartIR: Composing SPMD Partitioning Strategies for Machine Learning.

Sami Alabed, Bart Chrzaszcz, Juliana Franco, Dominik Grewe, Dougal Maclaurin, James Molloy, Tom Natan, Tamara Norman, Xiaoyue Pan, Adam Paszke, Norman A. Rink, Michael Schaarschmidt, Timur Sitdikov, Agnieszka Swietlik, Dimitrios Vytiniotis, Joel Wee

CoRR, 2024

2023

You Only Linearize Once: Tangents Transpose to Gradients.

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

Proc. ACM Program. Lang., January, 2023

Infix-Extensible Record Types for Tabular Data.

Adam Paszke, Ningning Xie

Proceedings of the 8th ACM SIGPLAN International Workshop on Type-Driven Development, 2023

2022

The Foil: Capture-Avoiding Substitution With No Sharp Edges.

Dougal Maclaurin, Alexey Radul, Adam Paszke

Proceedings of the 34th Symposium on Implementation and Application of Functional Languages, 2022

2021

Getting to the point: index sets and parallelism-preserving autodiff for pointful array programming.

Adam Paszke, Daniel D. Johnson, David Duvenaud, Dimitrios Vytiniotis, Alexey Radul, Matthew J. Johnson, Jonathan Ragan-Kelley, Dougal Maclaurin

Proc. ACM Program. Lang., 2021

Automap: Towards Ergonomic Automated Parallelism for ML Models.

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

CoRR, 2021

Memory-efficient array redistribution through portable collective communication.

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

CoRR, 2021

Decomposing reverse-mode automatic differentiation.

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

CoRR, 2021

Tensors Fitting Perfectly.

Adam Paszke, Brennan Saeta

CoRR, 2021

Parallelism-preserving automatic differentiation for second-order array languages.

Adam Paszke, Matthew J. Johnson, Roy Frostig, Dougal Maclaurin

Proceedings of the FHPNC 2021: Proceedings of the 9th ACM SIGPLAN International Workshop on Functional High-Performance and Numerical Computing, 2021

2020

PyTorch Distributed: Experiences on Accelerating Data Parallel Training.

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

Proc. VLDB Endow., 2020

VC density of set systems defnable in tree-like graphs.

Adam Paszke, Michal Pilipczuk

CoRR, 2020

VC Density of Set Systems Definable in Tree-Like Graphs.

Adam Paszke, Michal Pilipczuk

Proceedings of the 45th International Symposium on Mathematical Foundations of Computer Science, 2020

2019

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 Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2017

Evaluation of neural network architectures for embedded systems.

Alfredo Canziani, Eugenio Culurciello, Adam Paszke

Proceedings of the IEEE International Symposium on Circuits and Systems, 2017

2016

ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.

Adam Paszke, Abhishek Chaurasia, Sangpil Kim, Eugenio Culurciello

CoRR, 2016

An Analysis of Deep Neural Network Models for Practical Applications.

Alfredo Canziani, Adam Paszke, Eugenio Culurciello

CoRR, 2016