Nir Baram

Reinforcement Learning · 2016–2017

A researcher in the Science corpus: 4 papers; trusts 83 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 63 citations into it; #1,224 of 2,242 by trust here, #1,750 by citations
• Artificial Intelligence — 43 citations into it; #16,326 of 35,345 by trust here, #19,873 by citations
• Computer Vision and Pattern Recognition — 20 citations into it
• Generative Models — 13 citations into it
• Optimization & Training — 10 citations into it
• Management Science and Operations Research — 9 citations into it
• Control and Systems Engineering — 7 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Nir Baram trusts overall

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

1. Richard S. Sutton
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Yann LeCun
7. Rajesh Ranganath
8. Ronald J. Williams
9. Andrew Barto
10. John Schulman

Who trusts Nir Baram overall

1. Rishabh Agarwal
2. Nathan Gavenski
3. Ermo Wei
4. Thanard Kurutach
5. Yijie Guo
6. Ruohan Wang
7. Evan Zheran Liu
8. Takayuki Osa
9. Serkan Cabi
10. Che Wang

Papers from Nir Baram's perspective

Ordered by how strongly the researchers Nir Baram 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. 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. 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
• Spatio-Temporal Abstractions in Reinforcement Learning Through Neural Encoding (2016) — 4 citations