Gergely Neu

Reinforcement Learning · 2007–2019

A researcher in the Science corpus: 5 papers; trusts 101 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

Whom Gergely Neu trusts overall

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

  1. Richard S. Sutton
  2. Martin L. Puterman
  3. Csaba Szepesvari
  4. Nicolò Cesa-Bianchi
  5. Elad Hazan
  6. Shai Shalev-Shwartz
  7. J. Nieuwenhuis
  8. Shun-ichi Amari
  9. Andrew Barto
  10. Michael L. Littman

Who trusts Gergely Neu overall

  1. Manuel Lopes
  2. Kun Li
  3. Nathan D. Ratliff
  4. Ofir Nachum
  5. Bo Dai
  6. Kavosh Asadi
  7. J. Bagnell
  8. Saurabh Arora
  9. Zhifei Shao
  10. Joel W. Burdick

Papers from Gergely Neu's perspective

Ordered by how strongly the researchers Gergely Neu 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. Gradient-based learning applied to document recognition
  5. Long Short-Term Memory
  6. ImageNet: A large-scale hierarchical image database
  7. Attention is All you Need (Neural Information Processing Systems)
  8. Learning Multiple Layers of Features from Tiny Images
  9. Reinforcement Learning: An Introduction
  10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers