Jacob Sacks

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 38 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Jacob Sacks trusts overall

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

  1. Pieter Abbeel
  2. Diederik P. Kingma
  3. Sergey Levine
  4. Geoffrey E. Hinton
  5. Yuval Tassa
  6. Volodymyr Mnih
  7. E. Todorov
  8. Richard S. Sutton
  9. Elad Hazan
  10. Yoshua Bengio

Papers from Jacob Sacks's perspective

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

  1. ImageNet classification with deep convolutional neural networks
  2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  4. Gradient-based learning applied to document recognition
  5. Long Short-Term Memory
  6. Attention is All you Need (Neural Information Processing Systems)
  7. ImageNet: A large-scale hierarchical image database
  8. Learning Multiple Layers of Features from Tiny Images
  9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers