Oron Anschel

Reinforcement Learning · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 76 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 54 citations into it; #1,225 of 2,242 by trust here, #1,750 by citations
• Artificial Intelligence — 36 citations into it; #16,327 of 35,345 by trust here, #19,873 by citations
• Computer Vision and Pattern Recognition — 18 citations into it
• Generative Models — 13 citations into it
• Optimization & Training — 10 citations into it
• Management Science and Operations Research — 9 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it
• Control and Systems Engineering — 5 citations into it

Whom Oron Anschel trusts overall

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

1. Richard S. Sutton
2. Hado van Hasselt
3. Geoffrey E. Hinton
4. Volodymyr Mnih
5. Alex Krizhevsky
6. Diederik P. Kingma
7. Andrew Barto
8. Yann LeCun
9. Martin A. Riedmiller
10. Yoshua Bengio

Who trusts Oron Anschel overall

1. Rishabh Agarwal
2. Che Wang
3. Arsenii Kuznetsov
4. Pingli Lv
5. Yuxi Li
6. A. Su
7. Yue Wu
8. Dale Schuurmans
9. Mohammad Norouzi
10. Zijian Zhou

Papers from Oron Anschel's perspective

Ordered by how strongly the researchers Oron Anschel 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning (2016) — 367 citations
• Model-based Adversarial Imitation Learning (2016) — 46 citations
• End-to-End Differentiable Adversarial Imitation Learning (2017) — 114 citations