Alex Ray

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 6 papers; trusts 55 researchers and is trusted by 136 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 208 citations into it; #4,461 of 35,345 by trust here, #338 by citations
• Reinforcement Learning — 102 citations into it; #365 of 2,242 by trust here, #118 by citations
• Computer Vision and Pattern Recognition — 88 citations into it; #3,465 of 21,575 by trust here, #2,872 by citations
• Control and Systems Engineering — 64 citations into it
• Robotics & Control — 23 citations into it; #546 of 609 by trust here, #53 by citations
• Computational Theory and Mathematics — 21 citations into it
• Optimization & Training — 17 citations into it
• Management Science and Operations Research — 16 citations into it

Whom Alex Ray trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. David G. Lowe
5. Ilya Sutskever
6. Alex Graves
7. Richard S. Sutton
8. Alex Krizhevsky
9. E. Todorov
10. Jimmy Ba

Who trusts Alex Ray overall

1. Sergey Levine
2. Pieter Abbeel
3. Michael Janner
4. Ofir Nachum
5. Marcin Andrychowicz
6. Shansi Zhang
7. Chelsea Finn
8. Benjamin Eysenbach
9. Ruslan Salakhutdinov
10. Trevor Darrell

Papers from Alex Ray's perspective

Ordered by how strongly the researchers Alex Ray 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Training language models to follow instructions with human feedback (2022) — 22,660 citations
• Domain randomization for transferring deep neural networks from simulation to the real world (2017) — 3,883 citations
• Evaluating Large Language Models Trained on Code (2021) — 10,663 citations
• Learning dexterous in-hand manipulation (2018) — 2,231 citations
• Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research (2018) — 642 citations
• Hindsight Experience Replay (2017) — 2,808 citations