Noah Siegel

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 5 papers; trusts 25 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

Whom Noah Siegel trusts overall

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

  1. Richard S. Sutton
  2. Diederik P. Kingma
  3. John Schulman
  4. Geoffrey E. Hinton
  5. Martin A. Riedmiller
  6. E. Todorov
  7. Sergey Levine
  8. Yuval Tassa
  9. Jimmy Ba
  10. Andrew Barto

Who trusts Noah Siegel overall

  1. Tianhe Yu
  2. Aviral Kumar
  3. Sergey Levine
  4. Scott Fujimoto
  5. Karl Pertsch
  6. Rafael Rafailov
  7. Rafael Figueiredo Prudencio
  8. Ziyun Wang
  9. Caglar Gulcehre
  10. David Brandfonbrener

Papers from Noah Siegel's perspective

Ordered by how strongly the researchers Noah Siegel 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. 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