Jing Peng

Reinforcement Learning · with Williams · 1990–2006

A researcher in the Science corpus: 8 papers; trusts 30 researchers and is trusted by 205 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 31 citations into it; #204 of 2,242 by trust here, #652 by citations
• Artificial Intelligence — 9 citations into it; #18,274 of 35,345 by trust here, #19,439 by citations
• Game & Decision Theory — 8 citations into it
• Statistical Learning Theory — 8 citations into it
• Management Science and Operations Research — 7 citations into it; #7,877 of 11,946 by trust here, #11,755 by citations
• Optimization & Training — 7 citations into it; #349 of 2,601 by trust here, #804 by citations

Whom Jing Peng trusts overall

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

1. Richard S. Sutton
2. Ronald J. Williams
3. Andrew W. Moore
4. Andrew Barto
5. Michael L. Littman
6. Bernhard Schölkopf
7. Dimitri P. Bertsekas
8. Gavin Adrian Rummery
9. Christopher J. C. H. Watkins
10. Christopher G. Atkeson

Who trusts Jing Peng overall

1. J. Urgen Schmidhuber
2. Sergey Levine
3. Richard S. Sutton
4. Yoshua Bengio
5. Pieter Abbeel
6. Mohammad Norouzi
7. Ofir Nachum
8. Michael L. Littman
9. Satinder Singh
10. Trevor Darrell

Papers from Jing Peng's perspective

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

Their papers

• SVM vs regularized least squares classification (2004) — 330 citations
• RVσ(t): a unifying approach to performance and convergence in online multiagent learning (2006) — 10 citations
• Locally Adaptive Metric Nearest-Neighbor Classification (2002) — 356 citations
• Performance Bounded Reinforcement Learning in Strategic Interactions (2004) — 44 citations
• Incremental multi-step Q-learning (1994)
• Efficient Learning and Planning Within the Dyna Framework (1993)
• Function Optimization using Connectionist Reinforcement Learning Algorithms (1991)
• An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories (1990)