Sven Gowal

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 14 papers; trusts 680 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 577 citations into it; #6,832 of 35,345 by trust here, #2,425 by citations
• Safety, Robustness & Interpretability — 275 citations into it; #659 of 2,251 by trust here, #426 by citations
• Computer Vision and Pattern Recognition — 246 citations into it; #20,396 of 21,575 by trust here, #6,281 by citations
• Reinforcement Learning — 151 citations into it
• Optimization & Training — 72 citations into it
• Signal Processing — 57 citations into it
• Natural Language Processing — 53 citations into it
• Management Science and Operations Research — 51 citations into it

Whom Sven Gowal trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Ian J. Goodfellow
5. Diederik P. Kingma
6. Christian Szegedy
7. Yoshua Bengio
8. Yann LeCun
9. Ilya Sutskever
10. Nicholas Carlini

Who trusts Sven Gowal overall

1. Sylvestre-Alvise Rebuffi
2. Huan Zhang
3. Eric Wong
4. Zekai Wang
5. Sungyoon Lee
6. Tianyu Pang
7. Shiqi Wang
8. Francesco Croce
9. Anqi Mao
10. Weili Nie

Papers from Sven Gowal's perspective

Ordered by how strongly the researchers Sven Gowal trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Competition-level code generation with AlphaCode (2022) — 2,356 citations
• Adversarial Robustness through Local Linearization (2019) — 333 citations
• Towards Robust Image Classification Using Sequential Attention Models (2019) — 72 citations
• Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation (2019) — 184 citations
• Training verified learners with learned verifiers (2018) — 176 citations
• On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models (2018) — 640 citations
• Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations (2019) — 60 citations
• Improving Robustness using Generated Data (2021) — 379 citations
• Challenges of real-world reinforcement learning: definitions, benchmarks and analysis (2021) — 780 citations
• A Dual Approach to Scalable Verification of Deep Networks (2018) — 416 citations
• Fixing Data Augmentation to Improve Adversarial Robustness (2021) — 319 citations
• Data Augmentation Can Improve Robustness (2021) — 411 citations
• Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples (2020) — 378 citations
• An empirical investigation of the challenges of real-world reinforcement learning (2020) — 140 citations