| On the effectiveness of interval bound propagation for training verifiably robust models S Gowal, K Dvijotham, R Stanforth, R Bunel, C Qin, J Uesato, ... arXiv preprint arXiv:1810.12715, 2018 | 331* | 2018 |
| A Dual Approach to Scalable Verification of Deep Networks. K Dvijotham, R Stanforth, S Gowal, TA Mann, P Kohli UAI 1 (2), 3, 2018 | 296 | 2018 |
| Adversarial robustness through local linearization C Qin, J Martens, S Gowal, D Krishnan, K Dvijotham, A Fawzi, S De, ... Advances in Neural Information Processing Systems 32, 2019 | 164 | 2019 |
| Towards stable and efficient training of verifiably robust neural networks H Zhang, H Chen, C Xiao, S Gowal, R Stanforth, B Li, D Boning, CJ Hsieh arXiv preprint arXiv:1906.06316, 2019 | 146 | 2019 |
| Training verified learners with learned verifiers K Dvijotham, S Gowal, R Stanforth, R Arandjelovic, B O'Donoghue, ... arXiv preprint arXiv:1805.10265, 2018 | 124 | 2018 |
| Challenges of real-world reinforcement learning: definitions, benchmarks and analysis G Dulac-Arnold, N Levine, DJ Mankowitz, J Li, C Paduraru, S Gowal, ... Machine Learning 110 (9), 2419-2468, 2021 | 99* | 2021 |
| Uncovering the limits of adversarial training against norm-bounded adversarial examples S Gowal, C Qin, J Uesato, T Mann, P Kohli arXiv preprint arXiv:2010.03593, 2020 | 96 | 2020 |
| Achieving verified robustness to symbol substitutions via interval bound propagation PS Huang, R Stanforth, J Welbl, C Dyer, D Yogatama, S Gowal, ... arXiv preprint arXiv:1909.01492, 2019 | 94 | 2019 |
| Fixing data augmentation to improve adversarial robustness SA Rebuffi, S Gowal, DA Calian, F Stimberg, O Wiles, T Mann arXiv preprint arXiv:2103.01946, 2021 | 60* | 2021 |
| An alternative surrogate loss for pgd-based adversarial testing S Gowal, J Uesato, C Qin, PS Huang, T Mann, P Kohli arXiv preprint arXiv:1910.09338, 2019 | 45 | 2019 |
| Towards robust image classification using sequential attention models D Zoran, M Chrzanowski, PS Huang, S Gowal, A Mott, P Kohli Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 40 | 2020 |
| Graph based distributed control of non-holonomic vehicles endowed with local positioning information engaged in escorting missions R Falconi, S Gowal, A Martinoli 2010 IEEE International Conference on Robotics and Automation, 3207-3214, 2010 | 40 | 2010 |
| Achieving robustness in the wild via adversarial mixing with disentangled representations S Gowal, C Qin, PS Huang, T Cemgil, K Dvijotham, T Mann, P Kohli Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 34 | 2020 |
| A new collision warning system for lead vehicles in rear-end collisions A Cabrera, S Gowal, A Martinoli 2012 IEEE Intelligent Vehicles Symposium, 674-679, 2012 | 32 | 2012 |
| Beyond greedy ranking: Slate optimization via list-CVAE R Jiang, S Gowal, TA Mann, DJ Rezende arXiv preprint arXiv:1803.01682, 2018 | 31* | 2018 |
| Verification of non-linear specifications for neural networks C Qin, B O'Donoghue, R Bunel, R Stanforth, S Gowal, J Uesato, ... arXiv preprint arXiv:1902.09592, 2019 | 30 | 2019 |
| A framework for robustness certification of smoothed classifiers using f-divergences KD Dvijotham, J Hayes, B Balle, Z Kolter, C Qin, A Gyorgy, K Xiao, ... | 23 | 2020 |
| Efficient neural network verification with exactness characterization KD Dvijotham, R Stanforth, S Gowal, C Qin, S De, P Kohli Uncertainty in artificial intelligence, 497-507, 2020 | 19 | 2020 |
| Comment on: A Validated Mathematical Model of Cell-Mediated Immune Response to Tumor Growth S Gowal, M de Giacomi, JY Le Boudec Cancer Research 67 (17), 8419, 2007 | 17* | 2007 |
| Learning from delayed outcomes via proxies with applications to recommender systems TA Mann, S Gowal, A Gyorgy, H Hu, R Jiang, B Lakshminarayanan, ... International Conference on Machine Learning, 4324-4332, 2019 | 16* | 2019 |
Pushmeet KohliDeepMindEmail confirmado em google.com
