| Automatic differentiation in pytorch A Paszke, S Gross, S Chintala, G Chanan, E Yang, Z DeVito, Z Lin, ... | 7121 | 2017 |
| Pytorch: An imperative style, high-performance deep learning library A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... arXiv preprint arXiv:1912.01703, 2019 | 5272 | 2019 |
| Learning disentangled representations with semi-supervised deep generative models N Siddharth, B Paige, JW Van de Meent, A Desmaison, ND Goodman, ... arXiv preprint arXiv:1706.00400, 2017 | 224 | 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 | 131 | 2019 |
| Pytorch: An imperative style, high-performance deep learning library B Steiner, Z DeVito, S Chintala, S Gross, A Paszke, F Massa, A Lerer, ... Advances in Neural Information Processing Systems 32, 8026-8037, 2019 | 90 | 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 | 61 | 2016 |
| Adaptive neural compilation R Bunel, A Desmaison, P Kohli, PHS Torr, MP Kumar arXiv preprint arXiv:1605.07969, 2016 | 42 | 2016 |
| Efficient continuous relaxations for dense CRF A Desmaison, R Bunel, P Kohli, PHS Torr, MP Kumar European conference on computer vision, 818-833, 2016 | 28 | 2016 |
| Learning to superoptimize programs R Bunel, A Desmaison, MP Kumar, PHS Torr, P Kohli arXiv preprint arXiv:1611.01787, 2016 | 20 | 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 | 17* | 2017 |
| 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 | 8 | 2020 |
| Inducing interpretable representations with variational autoencoders N Siddharth, B Paige, A Desmaison, JW Van de Meent, F Wood, ... arXiv preprint arXiv:1611.07492, 2016 | 8 | 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 | 6 | 2019 |
| Learning disentangled representations in deep generative models N Siddharth, B Paige, A Desmaison, JW van de Meent, F Wood, ... | 3 | 2016 |
| Learning Disentangled Representations in Deep Generative Models.(2016) N Siddharth, B Paige, A Desmaison, JW van de Meent, F Wood, ... Google Scholar Google Scholar Digital Library Digital Library, 2016 | 2 | 2016 |
| Power consumption analysis of parallel algorithms on GPUs F Magoulès, AKC Ahamed, A Desmaison, JC Léchenet, F Mayer, ... 2014 IEEE Intl Conf on High Performance Computing and Communications, 2014 …, 2014 | 2 | 2014 |
| Fast and green computing with graphics processing units for solving sparse linear systems AKC Ahamed, A Desmaison, F Magoulès 2014 IEEE Intl Conf on High Performance Computing and Communications, 2014 …, 2014 | 2 | 2014 |
| 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 | | 2021 |
| Optimization for and by machine learning A Desmaison University of Oxford, 2019 | | 2019 |
| Learning to superoptimize programs PK Mudigonda, R Bunel, A Desmaison, P Kohli, PHS Torr Computational and Biological Learning Society, 2017 | | 2017 |
