Faraz Torabi

Reinforcement Learning · 2018–2019

A researcher in the Science corpus: 6 papers; trusts 280 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

Whom Faraz Torabi trusts overall

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

  1. Pieter Abbeel
  2. Sergey Levine
  3. Diederik P. Kingma
  4. Richard S. Sutton
  5. John Schulman
  6. Abhishek Gupta
  7. Andrew Y. Ng
  8. Stefan Schaal
  9. Geoffrey E. Hinton
  10. Jonathan Ho

Who trusts Faraz Torabi overall

  1. Nathan Gavenski
  2. Garrett Warnell
  3. Ruohan Zhang
  4. Peter Stone
  5. Daniel S. Brown
  6. Gabriel Recchia
  7. Felipe Meneguzzi
  8. Scott Niekum
  9. Michael Luck
  10. Odinaldo Rodrigues

Papers from Faraz Torabi's perspective

Ordered by how strongly the researchers Faraz Torabi 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. Attention is All you Need (Neural Information Processing Systems)
  6. Gradient-based learning applied to document recognition
  7. ImageNet: A large-scale hierarchical image database
  8. GENERATIVE ADVERSARIAL NETS
  9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers