Chongli Qin
Chongli Qin
Research Scientist, DeepMind
Verified email at google.com
Title
Cited by
Cited by
Year
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
7052020
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
1832018
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
1272019
Adversarial robustness through local linearization
C Qin, J Martens, S Gowal, D Krishnan, K Dvijotham, A Fawzi, S De, ...
arXiv preprint arXiv:1907.02610, 2019
1032019
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
932018
Power law tails in phylogenetic systems
C Qin, LJ Colwell
Proceedings of the National Academy of Sciences 115 (4), 690-695, 2018
352018
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
342019
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
302019
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
242019
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
232020
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
152020
A Framework for robustness Certification of Smoothed Classifiers using F-Divergences.
KD Dvijotham, J Hayes, B Balle, Z Kolter, C Qin, A György, K Xiao, ...
ICLR, 2020
132020
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
112020
On the effectiveness of interval bound propagation for training verifiably robust models. arXiv 2018
S Gowal, K Dvijotham, R Stanforth, R Bunel, C Qin, J Uesato, ...
arXiv preprint arXiv:1810.12715, 0
9
Training Generative Adversarial Networks by Solving Ordinary Differential Equations
C Qin, Y Wu, JT Springenberg, A Brock, J Donahue, TP Lillicrap, P Kohli
arXiv preprint arXiv:2010.15040, 2020
32020
Power Law Tails In Phylogenetic Systems
L Colwell, C Qin
National Academy of Sciences, 2018
2018
Supplementary: Training Generative Adversarial Networks by Solving Ordinary Differential Equations
C Qin, Y Wu, JT Springenberg, A Brock, J Donahue, TP Lillicrap, P Kohli
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Articles 1–17