Oleg Klimov

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 4 papers; trusts 76 researchers and is trusted by 450 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 80 citations into it; #4,449 of 35,345 by trust here, #4,316 by citations
• Reinforcement Learning — 72 citations into it; #75 of 2,242 by trust here, #62 by citations
• Management Science and Operations Research — 22 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Optimization & Training — 12 citations into it; #163 of 2,601 by trust here, #92 by citations
• Evaluation & Benchmarks — 10 citations into it; #2,527 of 3,147 by trust here, #1,301 by citations
• Model Architectures — 9 citations into it
• Cognitive Neuroscience — 6 citations into it

Whom Oleg Klimov trusts overall

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

1. J. Urgen Schmidhuber
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. John Schulman
5. Richard S. Sutton
6. Sepp Hochreiter
7. Yoshua Bengio
8. Marc G. Bellemare
9. Volodymyr Mnih
10. Alex Krizhevsky

Who trusts Oleg Klimov overall

1. Sergey Levine
2. Pieter Abbeel
3. Yilun Du
4. Kimin Lee
5. Danijar Hafner
6. Roberta Raileanu
7. Yoshua Bengio
8. Chelsea Finn
9. Trevor Darrell
10. Abhinav Kumar Gupta

Papers from Oleg Klimov's perspective

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

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

Their papers

• Exploration by Random Network Distillation (2018) — 1,706 citations
• Quantifying Generalization in Reinforcement Learning (2018) — 782 citations
• Gotta Learn Fast: A New Benchmark for Generalization in RL (2018) — 189 citations
• Proximal Policy Optimization Algorithms (2017) — 29,461 citations