Georg Ostrovski

Reinforcement Learning · 2015–2018

A researcher in the Science corpus: 9 papers; trusts 196 researchers and is trusted by 462 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 154 citations into it; #101 of 2,242 by trust here, #25 by citations
• Artificial Intelligence — 150 citations into it; #1,019 of 35,345 by trust here, #977 by citations
• Computer Vision and Pattern Recognition — 50 citations into it; #9,563 of 21,575 by trust here, #12,992 by citations
• Management Science and Operations Research — 45 citations into it; #10,330 of 11,946 by trust here, #4,430 by citations
• Generative Models — 39 citations into it; #896 of 1,956 by trust here, #1,415 by citations
• Evaluation & Benchmarks — 18 citations into it
• Cognitive Neuroscience — 17 citations into it; #1,767 of 10,852 by trust here, #23 by citations
• Optimization & Training — 16 citations into it

Whom Georg Ostrovski trusts overall

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

1. Richard S. Sutton
2. Marc G. Bellemare
3. Aäron van den Oord
4. Alex Krizhevsky
5. Geoffrey E. Hinton
6. Diederik P. Kingma
7. Koray Kavukcuoglu
8. Yoshua Bengio
9. R´emi Munos
10. Volodymyr Mnih

Who trusts Georg Ostrovski overall

1. Sergey Levine
2. Danijar Hafner
3. Brendan O'Donoghue
4. Marc G. Bellemare
5. Pieter Abbeel
6. Yoshua Bengio
7. Yilun Du
8. Kamyar Azizzadenesheli
9. Yuxi Li
10. Shangtong Zhang

Papers from Georg Ostrovski's perspective

Ordered by how strongly the researchers Georg Ostrovski 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. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Unifying Count-Based Exploration and Intrinsic Motivation (2016) — 1,688 citations
• Hybrid computing using a neural network with dynamic external memory (2016) — 1,765 citations
• Rainbow: Combining Improvements in Deep Reinforcement Learning (2017) — 2,665 citations
• Count-Based Exploration with Neural Density Models (2017) — 695 citations
• Implicit Quantile Networks for Distributional Reinforcement Learning (2018) — 714 citations
• Increasing the Action Gap: New Operators for Reinforcement Learning (2015) — 168 citations
• Autoregressive Quantile Networks for Generative Modeling (2018) — 94 citations
• Recurrent Experience Replay in Distributed Reinforcement Learning (2018) — 568 citations
• Human-level control through deep reinforcement learning (2015) — 32,431 citations