Sven Gowal
Sven Gowal
Verified email at
Cited by
Cited by
A Dual Approach to Scalable Verification of Deep Networks.
K Dvijotham, R Stanforth, S Gowal, TA Mann, P Kohli
UAI 1 (2), 3, 2018
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
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
Training verified learners with learned verifiers
K Dvijotham, S Gowal, R Stanforth, R Arandjelovic, B O'Donoghue, ...
arXiv preprint arXiv:1805.10265, 2018
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
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
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
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
An empirical investigation of the challenges of real-world reinforcement learning
G Dulac-Arnold, N Levine, DJ Mankowitz, J Li, C Paduraru, S Gowal, ...
arXiv preprint arXiv:2003.11881, 2020
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
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
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
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
Beyond greedy ranking: Slate optimization via list-CVAE
R Jiang, S Gowal, TA Mann, DJ Rezende
arXiv preprint arXiv:1803.01682, 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
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, ...
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
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
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
A realistic simulator for the design and evaluation of intelligent vehicles
S Gowal, Y Zhang, A Martinoli
13th International IEEE Conference on Intelligent Transportation Systems …, 2010
The system can't perform the operation now. Try again later.
Articles 1–20