An Analysis of Deep Neural Network Models for Practical Applications. - Alfredo Canziani; Adam Paszke; Eugenio Culurciello (CoRR, 2016)

ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. - Adam Paszke; Abhishek Chaurasia; Sangpil Kim; Eugenio Culurciello (CoRR, 2016)

Evaluation of neural network architectures for embedded systems. - Alfredo Canziani; Eugenio Culurciello; Adam Paszke (ISCAS, 2017)

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; et al. (NeurIPS, 2019)

VC Density of Set Systems Definable in Tree-Like Graphs. - Adam Paszke; Michal Pilipczuk (MFCS, 2020)

VC density of set systems defnable in tree-like graphs. - Adam Paszke; Michal Pilipczuk (CoRR, 2020)

PyTorch Distributed: Experiences on Accelerating Data Parallel Training. - Shen Li; Yanli Zhao; Rohan Varma; Omkar Salpekar; Pieter Noordhuis; Teng Li 009; Adam Paszke; Jeff Smith; Brian Vaughan; Pritam Damania; Soumith Chintala (Proc. VLDB Endow., 2020)

Parallelism-preserving automatic differentiation for second-order array languages. - Adam Paszke; Matthew J. Johnson 002; Roy Frostig; Dougal Maclaurin (FHPNC@ICFP, 2021)

Tensors Fitting Perfectly. - Adam Paszke; Brennan Saeta (CoRR, 2021)

Decomposing reverse-mode automatic differentiation. - Roy Frostig; Matthew J. Johnson 002; Dougal Maclaurin; Adam Paszke; Alexey Radul (CoRR, 2021)

Memory-efficient array redistribution through portable collective communication. - Norman A. Rink; Adam Paszke; Dimitrios Vytiniotis; Georg Stefan Schmid (CoRR, 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)

Getting to the point: index sets and parallelism-preserving autodiff for pointful array programming. - Adam Paszke; Daniel D. Johnson 001; David Duvenaud; Dimitrios Vytiniotis; Alexey Radul; Matthew J. Johnson 002; Jonathan Ragan-Kelley; Dougal Maclaurin (Proc. ACM Program. Lang., 2021)

The Foil: Capture-Avoiding Substitution With No Sharp Edges. - Dougal Maclaurin; Alexey Radul; Adam Paszke (IFL, 2022)

Infix-Extensible Record Types for Tabular Data. - Adam Paszke; Ningning Xie (TyDe@ICFP, 2023)

You Only Linearize Once: Tangents Transpose to Gradients. - Alexey Radul; Adam Paszke; Roy Frostig; Matthew J. Johnson 002; Dougal Maclaurin (Proc. ACM Program. Lang., 2023)

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; et al. (CoRR, 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; et al. (CoRR, 2024)

Parallel Algebraic Effect Handlers. - Ningning Xie; Daniel D. Johnson 001; Dougal Maclaurin; Adam Paszke (Proc. ACM Program. Lang., 2024)

PartIR: Composing SPMD Partitioning Strategies for Machine Learning. - Sami Alabed; Daniel Belov; Bart Chrzaszcz; Juliana Franco; Dominik Grewe; Dougal Maclaurin; James Molloy; Tom Natan; Tamara Norman; Xiaoyue Pan; Adam Paszke; Norman A. Rink; et al. (ASPLOS (1), 2025)