Simon Schmitt

Reinforcement Learning · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 73 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 131 citations into it; #885 of 2,242 by trust here, #445 by citations
• Artificial Intelligence — 119 citations into it; #10,993 of 35,345 by trust here, #5,390 by citations
• Computer Vision and Pattern Recognition — 31 citations into it
• Management Science and Operations Research — 19 citations into it; #4,832 of 11,946 by trust here, #1,867 by citations
• Computational Theory and Mathematics — 15 citations into it
• Optimization & Training — 14 citations into it; #2,124 of 2,601 by trust here, #1,178 by citations
• Model Architectures — 12 citations into it
• Cognitive Neuroscience — 10 citations into it

Whom Simon Schmitt trusts overall

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

1. Richard S. Sutton
2. Volodymyr Mnih
3. Geoffrey E. Hinton
4. Andrew Barto
5. Yoshua Bengio
6. Ronald J. Williams
7. Sepp Hochreiter
8. J. Urgen Schmidhuber
9. John Schulman
10. Hado van Hasselt

Who trusts Simon Schmitt overall

1. Danijar Hafner
2. Aleksei Petrenko
3. H. Francis Song
4. Sebastian Flennerhag
5. Wojciech M. Czarnecki
6. J. Pašukonis
7. Julian Schrittwieser
8. Zhuangdi Zhu
9. Kun Shao
10. Jimmy Ba

Papers from Simon Schmitt's perspective

Ordered by how strongly the researchers Simon Schmitt 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Mastering Atari, Go, chess and shogi by planning with a learned model (2019) — 2,607 citations
• Multi-task Deep Reinforcement Learning with PopArt (2018) — 363 citations
• Off-Policy Actor-Critic with Shared Experience Replay (2019) — 71 citations
• Kickstarting Deep Reinforcement Learning (2018) — 151 citations