Chongli Qin

According to our database, Chongli Qin authored at least 13 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

On a continuous time model of gradient descent dynamics and instability in deep learning.

Mihaela Rosca, Yan Wu, Chongli Qin, Benoit Dherin

Trans. Mach. Learn. Res., 2023

Feature Likelihood Score: Evaluating the Generalization of Generative Models Using Samples.

Marco Jiralerspong, Avishek Joey Bose, Ian Gemp, Chongli Qin, Yoram Bachrach, Gauthier Gidel

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2020

Improved protein structure prediction using potentials from deep learning.

Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Zídek, Alexander W. R. Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, Demis Hassabis

Nat., 2020

Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples.

Sven Gowal, Chongli Qin, Jonathan Uesato, Timothy A. Mann, Pushmeet Kohli

CoRR, 2020

Training Generative Adversarial Networks by Solving Ordinary Differential Equations.

Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andy Brock, Jeff Donahue, Timothy P. Lillicrap, Pushmeet Kohli

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A Framework for robustness Certification of Smoothed Classifiers using F-Divergences.

Krishnamurthy (Dj) Dvijotham, Jamie Hayes, Borja Balle, J. Zico Kolter, Chongli Qin, András György, Kai Xiao, Sven Gowal, Pushmeet Kohli

Proceedings of the 8th International Conference on Learning Representations, 2020

Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations.

Sven Gowal, Chongli Qin, Po-Sen Huang, A. Taylan Cemgil, Krishnamurthy Dvijotham, Timothy A. Mann, Pushmeet Kohli

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

An Alternative Surrogate Loss for PGD-based Adversarial Testing.

Sven Gowal, Jonathan Uesato, Chongli Qin, Po-Sen Huang, Timothy A. Mann, Pushmeet Kohli

CoRR, 2019

Efficient Neural Network Verification with Exactness Characterization.

Krishnamurthy (Dj) Dvijotham, Robert Stanforth, Sven Gowal, Chongli Qin, Soham De, Pushmeet Kohli

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Adversarial Robustness through Local Linearization.

Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Verification of Non-Linear Specifications for Neural Networks.

Chongli Qin, Krishnamurthy (Dj) Dvijotham, Brendan O'Donoghue, Rudy Bunel, Robert Stanforth, Sven Gowal, Jonathan Uesato, Grzegorz Swirszcz, Pushmeet Kohli

Proceedings of the 7th International Conference on Learning Representations, 2019

Scalable Verified Training for Provably Robust Image Classification.

Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Arthur Mann, Pushmeet Kohli

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

2018

On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models.

Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy A. Mann, Pushmeet Kohli

CoRR, 2018