Adam Stooke

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 7 papers; trusts 297 researchers and is trusted by 41 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 198 citations into it; #5,830 of 35,345 by trust here, #7,010 by citations
• Reinforcement Learning — 152 citations into it; #663 of 2,242 by trust here, #433 by citations
• Computer Vision and Pattern Recognition — 64 citations into it; #12,719 of 21,575 by trust here, #9,722 by citations
• Management Science and Operations Research — 35 citations into it; #7,850 of 11,946 by trust here, #6,024 by citations
• Optimization & Training — 34 citations into it
• Representation & Self-Supervised Learning — 27 citations into it; #2,200 of 3,145 by trust here, #1,781 by citations
• Computational Theory and Mathematics — 23 citations into it
• Evaluation & Benchmarks — 22 citations into it

Whom Adam Stooke trusts overall

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

1. Geoffrey E. Hinton
2. John Schulman
3. Richard S. Sutton
4. Volodymyr Mnih
5. Marc G. Bellemare
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Yoshua Bengio
9. Sergey Levine
10. Kaiming He

Who trusts Adam Stooke overall

1. Steven Dalton
2. Pieter Abbeel
3. Yilun Du
4. Keiran Paster
5. Denis Yarats
6. Benjamin Eysenbach
7. Mengjiao Yang
8. Max Schwarzer
9. Vincenzo Lomonaco
10. N. Rudin

Papers from Adam Stooke's perspective

Ordered by how strongly the researchers Adam Stooke 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Reinforcement Learning with Augmented Data (2020) — 798 citations
• Open-Ended Learning Leads to Generally Capable Agents (2021) — 230 citations
• Decoupling Representation Learning from Reinforcement Learning (2020) — 402 citations
• Accelerated Methods for Deep Reinforcement Learning (2018) — 143 citations
• rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch (2019) — 101 citations
• Responsive Safety in Reinforcement Learning by PID Lagrangian Methods (2020) — 463 citations
• #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning (2016) — 866 citations