| Pytorch: An imperative style, high-performance deep learning library A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... Advances in neural information processing systems 32, 2019 | 53955 | 2019 |
| Automatic differentiation in pytorch A Paszke, S Gross, S Chintala, G Chanan, E Yang, Z DeVito, Z Lin, ... 31st Conference on Neural Information Processing Systems (NIPS 2017), 2017 | 14879 | 2017 |
| Advances in neural information processing systems A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... Curran Associates, Inc 32, 8024-8035, 2019 | 2455 | 2019 |
| Automatic differentiation in pytorch.(2017) A Paszke, S Gross, S Chintala, G Chanan, E Yang, Z DeVito, Z Lin, ... | 789 | 2017 |
| Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation J Ansel, E Yang, H He, N Gimelshein, A Jain, M Voznesensky, B Bao, ... Proceedings of the 29th ACM International Conference on Architectural …, 2024 | 441 | 2024 |
| Learning disentangled representations with semi-supervised deep generative models B Paige, JW Van De Meent, A Desmaison, N Goodman, P Kohli, F Wood, ... Advances in neural information processing systems 30, 2017 | 419 | 2017 |
| Pytorch: An imperative style, high-performance deep learning library. arXiv 2019 A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... arXiv preprint arXiv:1912.01703 10, 1912 | 321 | 1912 |
| Pytorch fsdp: experiences on scaling fully sharded data parallel Y Zhao, A Gu, R Varma, L Luo, CC Huang, M Xu, L Wright, H Shojanazeri, ... arXiv preprint arXiv:2304.11277, 2023 | 273 | 2023 |
| PyTorch: an imperative style A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... High-Performance Deep Learning Library 12, 2019 | 192 | 2019 |
| Playing doom with slam-augmented deep reinforcement learning S Bhatti, A Desmaison, O Miksik, N Nardelli, N Siddharth, PHS Torr arXiv preprint arXiv:1612.00380, 2016 | 98 | 2016 |
| Improved branch and bound for neural network verification via lagrangian decomposition A De Palma, R Bunel, A Desmaison, K Dvijotham, P Kohli, PHS Torr, ... arXiv preprint arXiv:2104.06718, 2021 | 68 | 2021 |
| Advances in Neural Information Processing Systems 32 ed H A Paszke, S Gross, F Massa Wallach et al 8024, 2019 | 66 | 2019 |
| Lagrangian decomposition for neural network verification R Bunel, A De Palma, A Desmaison, K Dvijotham, P Kohli, P Torr, ... Conference on Uncertainty in Artificial Intelligence, 370-379, 2020 | 65 | 2020 |
| Adaptive neural compilation RR Bunel, A Desmaison, PK Mudigonda, P Kohli, P Torr Advances in Neural Information Processing Systems 29, 2016 | 58 | 2016 |
| Andreas Kö pf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An … A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... Advances in neural information processing systems 32, 8-14, 2019 | 45 | 2019 |
| Learning to superoptimize programs R Bunel, A Desmaison, MP Kumar, PHS Torr, P Kohli International Conference on Learning Representations (ICLR), 2017 | 43 | 2017 |
| Pytorch: an imperative style, high-performance deep learning library. CoRR abs/1912.01703 (2019) A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... arXiv preprint arXiv:1912.01703, 1912 | 41 | 1912 |
| Efficient continuous relaxations for dense CRF A Desmaison, R Bunel, P Kohli, PHS Torr, MP Kumar Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 38 | 2016 |
| Efficient linear programming for dense CRFs T Ajanthan, A Desmaison, R Bunel, M Salzmann, PHS Torr, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 21 | 2017 |
| Inducing interpretable representations with variational autoencoders N Siddharth, B Paige, A Desmaison, JW Van de Meent, F Wood, ... arXiv preprint arXiv:1611.07492, 2016 | 14 | 2016 |
