| Improved protein structure prediction using potentials from deep learning AW Senior, R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin, ... Nature 577 (7792), 706-710, 2020 | 3324 | 2020 |
| 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 | 531 | 2018 |
| Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13) AW Senior, R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin, ... Proteins: structure, function, and bioinformatics 87 (12), 1141-1148, 2019 | 337 | 2019 |
| 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 | 336 | 2019 |
| 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 | 333 | 2020 |
| Scalable verified training for provably robust image classification S Gowal, KD Dvijotham, R Stanforth, R Bunel, C Qin, J Uesato, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 186 | 2019 |
| De novo structure prediction with deeplearning based scoring R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin, A Zidek, ... Annu Rev Biochem 77 (363-382), 6, 2018 | 153 | 2018 |
| 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 | 84 | 2019 |
| Power law tails in phylogenetic systems C Qin, LJ Colwell Proceedings of the National Academy of Sciences 115 (4), 690-695, 2018 | 68 | 2018 |
| 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 | 61 | 2020 |
| 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, ... International Conference on Learning Representations, 2020 | 58 | 2020 |
| 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 | 48 | 2019 |
| Training generative adversarial networks by solving ordinary differential equations C Qin, Y Wu, JT Springenberg, A Brock, J Donahue, T Lillicrap, P Kohli Advances in Neural Information Processing Systems 33, 5599-5609, 2020 | 40 | 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 | 33 | 2020 |
| Augustin ˇZıdek, Alexander WR Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, Koray … AW Senior, R Evans, J Jumper, J Kirkpatrick, L Sifre, T Green, C Qin Nature 577 (7792), 706-710, 2020 | 22 | 2020 |
| On the effectiveness of interval bound propagation for training verifiably robust models (2018) S Gowal, K Dvijotham, R Stanforth, R Bunel, C Qin, J Uesato, ... arXiv preprint arXiv:1810.12715, 2018 | 11 | 2018 |
| Machine learning for determining protein structures AW Senior, J Kirkpatrick, L Sifre, RA Evans, H Penedones, QIN Chongli, ... | 9 | 2021 |
| On a continuous time model of gradient descent dynamics and instability in deep learning M Rosca, Y Wu, C Qin, B Dherin arXiv preprint arXiv:2302.01952, 2023 | 5 | 2023 |
| Training more secure neural networks by using local linearity regularization C Qin, SA Gowal, S De, R Stanforth, J Martens, K Dvijotham, D Krishnan, ... US Patent 11,526,755, 2022 | 5 | 2022 |
| Training more secure neural networks by using local linearity regularization C Qin, SA Gowal, S De, R Stanforth, J Martens, K Dvijotham, D Krishnan, ... US Patent 11,775,830, 2023 | 1 | 2023 |
