H. Seung

Reinforcement Learning · 2004–2005

A researcher in the Science corpus: 2 papers; trusts 18 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

Whom H. Seung trusts overall

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

  1. Richard S. Sutton
  2. Christopher G. Atkeson
  3. Hajime Kimura
  4. Vijay R. Konda
  5. Nate Kohl
  6. Andrew Y. Ng
  7. Andrew Barto
  8. John N. Tsitsiklis
  9. Jonathan Baxter
  10. Stefan Schaal

Who trusts H. Seung overall

  1. Sergey Levine
  2. Pieter Abbeel
  3. Vikash Kumar
  4. Ignasi Clavera Gilaberte
  5. Jens Kober
  6. Jan Peters
  7. Chelsea Finn
  8. Ignasi Clavera
  9. J. Bagnell
  10. Nikos A. Vlassis

Papers from H. Seung's perspective

Ordered by how strongly the researchers H. Seung 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. Infinite-Horizon Policy-Gradient Estimation
  6. ImageNet classification with deep convolutional neural networks
  7. Gradient-based learning applied to document recognition
  8. Policy gradient reinforcement learning for fast quadrupedal locomotion
  9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  10. An Analysis of Actor/Critic Algorithms Using Eligibility Traces: Reinforcement Learning with Imperfect Value Function

Their papers