Takamitsu Matsubara

Reinforcement Learning · 2005–2014

A researcher in the Science corpus: 4 papers; trusts 18 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

Whom Takamitsu Matsubara trusts overall

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

  1. Kenji Doya
  2. Hajime Kimura
  3. Richard S. Sutton
  4. Ronald J. Williams
  5. Vijay R. Konda
  6. Maja J. Mataric
  7. B. Kendall
  8. Jonathan Baxter
  9. Jan Peters
  10. Shigenobu Kobayashi

Who trusts Takamitsu Matsubara overall

  1. Hao Wang
  2. Dit-Yan Yeung
  3. Sergey Levine
  4. Chelsea Finn
  5. Jan Peters

Papers from Takamitsu Matsubara's perspective

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

  1. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
  2. Reinforcement Learning: An Introduction
  3. Policy Gradient Methods for Reinforcement Learning with Function Approximation
  4. Actor-Critic Algorithms
  5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  6. ImageNet classification with deep convolutional neural networks
  7. Infinite-Horizon Policy-Gradient Estimation
  8. Gradient-based learning applied to document recognition
  9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  10. Attention is All you Need (Neural Information Processing Systems)

Their papers