| PyTorch: An Imperative Style, High-Performance Deep Learning Library A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... NeurIPS '19: Proceedings of the 33rd International Conference on Neural …, 2019 | 58964* | 2019 |
| ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation A Paszke, A Chaurasia, S Kim, E Culurciello arXiv preprint arXiv:1606.02147, 2016 | 2718 | 2016 |
| JAX: composable transformations of Python+NumPy programs J Bradbury, R Frostig, P Hawkins, MJ Johnson, C Leary, D Maclaurin, ... http://github.com/google/jax, 2018 | 2598 | 2018 |
| An Analysis of Deep Neural Network Models for Practical Applications A Canziani, A Paszke, E Culurciello arXiv preprint arXiv:1605.07678, 2016 | 1641 | 2016 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 1042 | 2023 |
| Automatic differentiation in pytorch.(2017) A Paszke, S Gross, S Chintala, G Chanan, E Yang, Z DeVito, Z Lin, ... | 711 | 2017 |
| PyTorch Distributed: Experiences on Accelerating Data Parallel Training S Li, Y Zhao, R Varma, O Salpekar, P Noordhuis, T Li, A Paszke, J Smith, ... Proceedings of the VLDB Endowment 13 (12), 3005-3018, 2020 | 518 | 2020 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 196 | 2024 |
| Pytorch: An imperative style, high-performance deep learning library, 2019 A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... arXiv preprint arXiv:1912.01703 10, 1912 | 193 | 1912 |
| & Lerer, A.(2017) A Paszke, S Gross, S Chintala, G Chanan, E Yang, Z DeVito Automatic differentiation in pytorch, 2017 | 68 | 2017 |
| Advances in Neural Information Processing Systems 32 ed H A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... Wallach et al 8024, 2019 | 52 | 2019 |
| Getting to the Point. Index Sets and Parallelism-Preserving Autodiff for Pointful Array Programming A Paszke, D Johnson, D Duvenaud, D Vytiniotis, A Radul, M Johnson, ... Proc. ACM Program. Lang. 5 (ICFP), 2021 | 49 | 2021 |
| Evaluation of neural network architectures for embedded systems A Canziani, E Culurciello, A Paszke 2017 IEEE international symposium on Circuits and systems (ISCAS), 1-4, 2017 | 48 | 2017 |
| You Only Linearize Once: Tangents Transpose to Gradients A Radul, A Paszke, R Frostig, MJ Johnson, D Maclaurin Proceedings of the ACM on Programming Languages 7 (POPL), 1246-1274, 2023 | 21 | 2023 |
| Decomposing reverse-mode automatic differentiation R Frostig, MJ Johnson, D Maclaurin, A Paszke, A Radul LAFI 2021, 2021 | 12 | 2021 |
| Automap: Towards Ergonomic Automated Parallelism for ML Models M Schaarschmidt, D Grewe, D Vytiniotis, A Paszke, GS Schmid, T Norman, ... ML for Systems (NeurIPS 2021), 2021 | 10 | 2021 |
| Parallelism-preserving automatic differentiation for second-order array languages A Paszke, MJ Johnson, R Frostig, D Maclaurin Proceedings of the 9th ACM SIGPLAN International Workshop on Functional High …, 2021 | 7 | 2021 |
| VC Density of Set Systems Definable in Tree-Like Graphs A Paszke, M Pilipczuk 45th International Symposium on Mathematical Foundations of Computer Science …, 2020 | 7 | 2020 |
| Pytorch: an imperative style, high-performance deep learning library. In eds. Wallach, H. et al A Paszke Advances in Neural Information Processing Systems, 8026-8037, 0 | 5 | |
| Memory-efficient array redistribution through portable collective communication NA Rink, A Paszke, D Vytiniotis, GS Schmid arXiv preprint arXiv:2112.01075, 2021 | 4 | 2021 |
