Követés
Chongli Qin
Chongli Qin
Research Scientist, DeepMind
E-mail megerősítve itt: google.com
Cím
Hivatkozott rá
Hivatkozott rá
Év
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
29162020
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
4622018
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
3132019
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
3052019
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
2782020
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
1602019
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
1442018
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
722019
Power law tails in phylogenetic systems
C Qin, LJ Colwell
Proceedings of the National Academy of Sciences 115 (4), 690-695, 2018
662018
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
582020
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
442019
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, ...
412020
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
332020
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
312020
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
202020
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
82018
Machine learning for determining protein structures
AW Senior, J Kirkpatrick, L Sifre, RA Evans, H Penedones, QIN Chongli, ...
62021
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
42023
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
32022
Finding a stationary point of a loss function by an iterative algorithm using a variable learning rate value
M Rosca, BRU Dherin, Y Wu, C Qin
US Patent App. 18/232,291, 2024
2024
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Cikkek 1–20