Alexander Robey

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 6 papers; trusts 56 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 213 citations into it
• Safety, Robustness & Interpretability — 108 citations into it
• Computer Vision and Pattern Recognition — 45 citations into it
• Large Language Models — 24 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Optimization & Training — 16 citations into it
• Reinforcement Learning — 14 citations into it
• Control and Systems Engineering — 13 citations into it

Whom Alexander Robey trusts overall

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

1. Geoffrey E. Hinton
2. Ian J. Goodfellow
3. Alex Krizhevsky
4. Radford M. Neal
5. Yann LeCun
6. Yoshua Bengio
7. Diederik P. Kingma
8. Kaiming He
9. Matthew D. Zeiler
10. Christian Szegedy

Who trusts Alexander Robey overall

1. Qiusi Zhan
2. Maksym Andriushchenko
3. Matt Jordan
4. Sungyoon Lee
5. Hyunjik Kim
6. Zhixiang Liang
7. Liangzu Peng

Papers from Alexander Robey's perspective

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

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

Their papers

• Jailbreaking Black Box Large Language Models in Twenty Queries (2023) — 1,568 citations
• Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks (2019) — 570 citations
• On the Sample Complexity of Stability Constrained Imitation Learning (2021) — 52 citations
• JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models (2024) — 556 citations
• SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks (2023) — 521 citations
• Adversarial Robustness with Semi-Infinite Constrained Learning (2021) — 50 citations