Kagan Tumer

Reinforcement Learning · 1999–2018

A researcher in the Science corpus: 18 papers; trusts 171 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

Whom Kagan Tumer trusts overall

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

  1. Richard S. Sutton
  2. David Hilton Wolpert
  3. Andrew Barto
  4. Geoffrey E. Hinton
  5. Michael L. Littman
  6. Diederik P. Kingma
  7. G. Tesauro
  8. Adrian K. Agogino
  9. Leslie Pack Kaelbling
  10. Jimmy Ba

Who trusts Kagan Tumer overall

  1. Adrian K. Agogino
  2. Pablo Hernandez-Leal
  3. Laëtitia Matignon
  4. Yihan Wang
  5. Scott Proper
  6. David Hilton Wolpert
  7. Akifumi Wachi
  8. Karl Tuyls
  9. Jingjing Cui
  10. Peter Sunehag

Papers from Kagan Tumer's perspective

Ordered by how strongly the researchers Kagan Tumer 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. Learning Multiple Layers of Features from Tiny Images
  10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers