Rohin Shah

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 4 papers; trusts 22 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 72 citations into it
• Reinforcement Learning — 41 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it
• Cognitive Neuroscience — 6 citations into it
• Statistical and Nonlinear Physics — 5 citations into it

Whom Rohin Shah trusts overall

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

1. Pieter Abbeel
2. Sergey Levine
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Jonathan Ho
6. Richard S. Sutton
7. Chelsea Finn
8. Yoshua Bengio
9. Alex Krizhevsky
10. John Schulman

Papers from Rohin Shah's perspective

Ordered by how strongly the researchers Rohin Shah 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• On the Utility of Learning about Humans for Human-AI Coordination (2019) — 562 citations
• Optimal Policies Tend To Seek Power (2019) — 130 citations
• The MAGICAL Benchmark for Robust Imitation (2020) — 41 citations
• Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2 (2024) — 377 citations