Longxin Lin

Reinforcement Learning · 1992–2009

A researcher in the Science corpus: 6 papers; trusts 10 researchers and is trusted by 879 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 12 citations into it; #26 of 2,242 by trust here, #208 by citations
• Model Architectures — 6 citations into it

Whom Longxin Lin trusts overall

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

1. Richard S. Sutton
2. Leslie Pack Kaelbling
3. David E. Rumelhart
4. Geoffrey E. Hinton
5. Ben J. A. Kröse
6. Andrew W. Moore
7. Ronald J. Williams
8. David Chapman
9. Peter S. Dayan
10. Paul J. Werbos

Who trusts Longxin Lin overall

1. J. Urgen Schmidhuber
2. Sergey Levine
3. Mark B. Ring
4. Hado van Hasselt
5. Richard S. Sutton
6. Justin A. Boyan
7. Yoshua Bengio
8. Shimon Whiteson
9. R´emi Munos
10. Zachary Chase Lipton

Papers from Longxin Lin's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. ImageNet Large Scale Visual Recognition Challenge
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet: A large-scale hierarchical image database

Their papers

• Biological inspirations, kinematics modeling, mechanism design and experiments on an undulating robotic fin inspired by Gymnarchus niloticus (2009) — 139 citations
• Reinforcement learning with hidden states (1993)
• Scaling Up Reinforcement Learning for Robot Control (1993)
• Self-improving reactive agents based on reinforcement learning, planning and teaching (1992)
• Reinforcement Learning for Robots Using Neural Networks (1992)
• Memory Approaches to Reinforcement Learning in Non-Markovian Domains (1992)