Nicholas K. Jong

Reinforcement Learning · 2003–2008

A researcher in the Science corpus: 6 papers; trusts 43 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 59 citations into it
• Artificial Intelligence — 26 citations into it; #7,111 of 35,345 by trust here, #15,866 by citations
• Computer Networks and Communications — 12 citations into it; #8,490 of 9,886 by trust here, #6,788 by citations
• Information Systems — 7 citations into it; #7,363 of 8,771 by trust here, #6,195 by citations
• Statistical Learning Theory — 6 citations into it

Whom Nicholas K. Jong trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Ben J. A. Kröse
4. G. Tesauro
5. Satinder Singh
6. Martin L. Puterman
7. Andrew W. Moore
8. Martin A. Riedmiller
9. Longxin Lin
10. Ronen I. Brafman

Who trusts Nicholas K. Jong overall

1. Alessandro Lazaric
2. Nan Jiang
3. Lihong Li
4. Oliver Kroemer
5. Todd Hester
6. Yaqi Duan
7. Peter Stone
8. Anirudh Vemula
9. Ofir Nachum
10. Marcello Restelli

Papers from Nicholas K. Jong's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Reinforcement Learning: An Introduction
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Attention is All you Need (Neural Information Processing Systems)
10. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning

Their papers

• Transferring Instances for Model-Based Reinforcement Learning (2008) — 150 citations
• Model-based function approximation in reinforcement learning (2007) — 74 citations
• The utility of temporal abstraction in reinforcement learning (2008) — 89 citations
• On the use of hybrid reinforcement learning for autonomic resource allocation (2007) — 127 citations
• A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation (2006) — 396 citations
• Learning Predictive State Representations (2003) — 148 citations