Daniel Strouse

Reinforcement Learning · 2019

A researcher in the Science corpus: 1 paper; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Daniel Strouse trusts overall

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

  1. Richard S. Sutton
  2. Ronald J. Williams
  3. Geoffrey E. Hinton
  4. Marc G. Bellemare
  5. J. Urgen Schmidhuber
  6. Diederik P. Kingma
  7. Yoshua Bengio
  8. John Schulman
  9. Andrew Barto

Papers from Daniel Strouse's perspective

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

  1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  2. ImageNet classification with deep convolutional neural networks
  3. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
  4. Gradient-based learning applied to document recognition
  5. Asynchronous Methods for Deep Reinforcement Learning
  6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  7. Long Short-Term Memory
  8. Attention is All you Need (Neural Information Processing Systems)
  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