Aleksandra Malysheva

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 Aleksandra Malysheva trusts overall

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

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

Papers from Aleksandra Malysheva's perspective

Ordered by how strongly the researchers Aleksandra Malysheva 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

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