| 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 | 44861 | 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 | 13736 | 2017 |
| Advances in neural information processing systems 32 A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... Curran Associates, Inc, 8024-8035, 2019 | 1470 | 2019 |
| Automatic differentiation in pytorch.(2017) A Paszke, S Gross, S Chintala, G Chanan, E Yang, Z DeVito, Z Lin, ... | 711 | 2017 |
| 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 | 400 | 2017 |
| 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 |
| 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 | 109 | 2023 |
| 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 |
| 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 | 72 | 2024 |
| 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 | 58 | 2020 |
| Adaptive neural compilation RR Bunel, A Desmaison, PK Mudigonda, P Kohli, P Torr Advances in Neural Information Processing Systems 29, 2016 | 56 | 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 | 52 | 2021 |
| 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 |
| Learning to superoptimize programs R Bunel, A Desmaison, MP Kumar, PHS Torr, P Kohli International Conference on Learning Representations (ICLR), 2017 | 41 | 2017 |
| 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 | 37 | 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 |
| PyTorch: An imperative style, high-performance deep learning library.(NeurIPS)(2019) A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... | 20 | 1912 |
| PyTorch: An imperative style, high-performance deep learning library. arXiv [cs. LG] A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... arXiv preprint arXiv:1912.01703, 2019 | 13 | 2019 |
| Inducing interpretable representations with variational autoencoders N Siddharth, B Paige, A Desmaison, JW Van de Meent, F Wood, ... arXiv preprint arXiv:1611.07492, 2016 | 13 | 2016 |
| Efficient relaxations for dense crfs with sparse higher-order potentials T Joy, A Desmaison, T Ajanthan, R Bunel, M Salzmann, P Kohli, PHS Torr, ... SIAM journal on imaging sciences 12 (1), 287-318, 2019 | 12 | 2019 |
