Aleksei Shpilman

Reinforcement Learning · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 23 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 21 citations into it
• Artificial Intelligence — 16 citations into it
• Model Architectures — 8 citations into it

Whom Aleksei Shpilman trusts overall

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

1. Maxim Egorov Stanford
2. Dimitri P. Bertsekas
3. Richard S. Sutton
4. Kaiming He
5. Sergey Ioffe
6. Geoffrey E. Hinton
7. Igor Mordatch
8. Christian Szegedy
9. Ashish Vaswani
10. Tingwu Wang

Papers from Aleksei Shpilman's perspective

Ordered by how strongly the researchers Aleksei Shpilman 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
5. Gradient-based learning applied to document recognition
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning (2019) — 25 citations
• Deep Multi-Agent Reinforcement Learning with Relevance Graphs (2018) — 50 citations