Yotam Doron

Reinforcement Learning · 2018–2020

A researcher in the Science corpus: 6 papers; trusts 102 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 144 citations into it; #659 of 2,242 by trust here, #204 by citations
• Artificial Intelligence — 134 citations into it; #10,201 of 35,345 by trust here, #2,759 by citations
• Computer Vision and Pattern Recognition — 87 citations into it
• Generative Models — 33 citations into it
• Control and Systems Engineering — 23 citations into it
• Optimization & Training — 23 citations into it
• Cognitive Neuroscience — 21 citations into it
• Management Science and Operations Research — 21 citations into it; #5,978 of 11,946 by trust here, #198 by citations

Whom Yotam Doron trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Diederik P. Kingma
4. Volodymyr Mnih
5. Geoffrey E. Hinton
6. Ronald J. Williams
7. Jimmy Ba
8. Alex Krizhevsky
9. Yoshua Bengio
10. Marc G. Bellemare

Who trusts Yotam Doron overall

1. Danijar Hafner
2. Pieter Abbeel
3. Sergey Levine
4. Josh Merel
5. Denis Yarats
6. Gabriel Dulac-Arnold
7. Kimin Lee
8. Amy Zhang
9. Caglar Gulcehre
10. Ilya Kostrikov

Papers from Yotam Doron's perspective

Ordered by how strongly the researchers Yotam Doron 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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

• IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures (2018) — 1,890 citations
• Deep Reinforcement Learning and the Deadly Triad (2018) — 287 citations
• Behaviour Suite for Reinforcement Learning (2019) — 212 citations
• Transformation-based Adversarial Video Prediction on Large-Scale Data (2020) — 95 citations
• dm_control: Software and Tasks for Continuous Control (2020) — 526 citations
• DeepMind Control Suite (2018) — 1,440 citations