Nahum Shimkin

Reinforcement Learning · 2002–2016

A researcher in the Science corpus: 3 papers; trusts 54 researchers and is trusted by 43 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 48 citations into it; #1,223 of 2,242 by trust here, #1,750 by citations
• Artificial Intelligence — 20 citations into it; #4,748 of 35,345 by trust here, #8,157 by citations
• Management Science and Operations Research — 6 citations into it

Whom Nahum Shimkin trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Reuven Y. Rubinstein
4. John N. Tsitsiklis
5. Satinder Singh
6. Hado van Hasselt
7. Longxin Lin
8. Geoffrey E. Hinton
9. Justin A. Boyan
10. Volodymyr Mnih

Who trusts Nahum Shimkin overall

1. J. Urgen Schmidhuber
2. Csaba Szepesvari
3. Dimitri P. Bertsekas
4. Rishabh Agarwal
5. Shie Mannor
6. Sean P. Meyn
7. Özgür Simsek
8. Michael L. Littman
9. Sridhar Mahadevan
10. Marlos C. Machado

Papers from Nahum Shimkin's perspective

Ordered by how strongly the researchers Nahum Shimkin trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Gradient-based learning applied to document recognition
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Reinforcement Learning: An Introduction
6. Long Short-Term Memory
7. Asynchronous Methods for Deep Reinforcement Learning
8. ImageNet: A large-scale hierarchical image database
9. Attention is All you Need (Neural Information Processing Systems)
10. Human-level control through deep reinforcement learning

Their papers

• Q-Cut - Dynamic Discovery of Sub-goals in Reinforcement Learning (2002) — 306 citations
• Basis Function Adaptation in Temporal Difference Reinforcement Learning (2005) — 208 citations
• Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning (2016) — 367 citations