Arash Tavakoli

Reinforcement Learning · 2017

A researcher in the Science corpus: 2 papers; trusts 52 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 49 citations into it; #1,870 of 2,242 by trust here, #2,092 by citations
• Artificial Intelligence — 34 citations into it; #27,602 of 35,345 by trust here, #23,731 by citations
• Management Science and Operations Research — 8 citations into it
• Computational Theory and Mathematics — 6 citations into it
• Control and Systems Engineering — 6 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Cognitive Neuroscience — 5 citations into it
• Computer Vision and Pattern Recognition — 5 citations into it

Whom Arash Tavakoli trusts overall

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

1. Richard S. Sutton
2. J. Urgen Schmidhuber
3. Dimitri P. Bertsekas
4. Diederik P. Kingma
5. John Schulman
6. Yoshua Bengio
7. Csaba Szepesvari
8. Ronald J. Williams
9. Geoffrey E. Hinton
10. Volodymyr Mnih

Who trusts Arash Tavakoli overall

1. Scott Fujimoto
2. Tom Van de Wiele
3. Gregory Farquhar
4. Robert Dadashi
5. Herke Van Hoof
6. Anton Raichuk
7. Yiding Jiang
8. David Warde-Farley
9. L'eonard Hussenot
10. N. Rudin

Papers from Arash Tavakoli's perspective

Ordered by how strongly the researchers Arash Tavakoli 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. 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. GENERATIVE ADVERSARIAL NETS

Their papers

• Action Branching Architectures for Deep Reinforcement Learning (2017) — 334 citations
• Time Limits in Reinforcement Learning (2017) — 196 citations