Matthew R. Fellows

Reinforcement Learning · 2002–2018

A researcher in the Science corpus: 4 papers; trusts 73 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 49 citations into it
• Artificial Intelligence — 40 citations into it
• Cognitive Neuroscience — 13 citations into it; #4,055 of 10,852 by trust here, #2,595 by citations
• Control and Systems Engineering — 9 citations into it
• Management Science and Operations Research — 9 citations into it
• Optimization & Training — 8 citations into it
• Probabilistic & Bayesian Methods — 7 citations into it
• Statistics and Probability — 7 citations into it

Whom Matthew R. Fellows trusts overall

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

1. Richard S. Sutton
2. John Schulman
3. Sergey Levine
4. Brian D. Ziebart
5. Radford M. Neal
6. Geoffrey E. Hinton
7. Ronald J. Williams
8. Jan Peters
9. Hado van Hasselt
10. Pieter Abbeel

Who trusts Matthew R. Fellows overall

1. John P. Donoghue
2. Brendan O'Donoghue
3. Niels Birbaumer
4. Mikhail A. Lebedev
5. Matthew A. Smith
6. Sung-Phil Kim
7. Ian Osband

Papers from Matthew R. Fellows's perspective

Ordered by how strongly the researchers Matthew R. Fellows 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. Reinforcement Learning: An Introduction
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• VIREL: A Variational Inference Framework for Reinforcement Learning (2018) — 59 citations
• Brain-machine interface: Instant neural control of a movement signal (2002) — 812 citations
• Reliability of signals from a chronically implanted, silicon-based electrode array in non-human primate primary motor cortex (2005) — 447 citations
• A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. (2005) — 1,143 citations