Alexander Sasha Vezhnevets

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 27 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 109 citations into it; #12,901 of 35,345 by trust here, #2,009 by citations
• Reinforcement Learning — 106 citations into it; #1,221 of 2,242 by trust here, #198 by citations
• Management Science and Operations Research — 19 citations into it
• Sociology and Political Science — 18 citations into it
• Model Architectures — 14 citations into it; #2,053 of 2,278 by trust here, #1,261 by citations
• Generative Models — 5 citations into it

Whom Alexander Sasha Vezhnevets trusts overall

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

1. Richard S. Sutton
2. Alex Graves
3. Doina Precup
4. Geoffrey E. Hinton
5. J. Urgen Schmidhuber
6. Diederik P. Kingma
7. Volodymyr Mnih
8. Peter S. Dayan
9. Sepp Hochreiter
10. Yoshua Bengio

Who trusts Alexander Sasha Vezhnevets overall

1. Sergey Levine
2. Balaraman Ravindran
3. Ofir Nachum
4. Fei Xia
5. Jeff Clune
6. Pieter Abbeel
7. Yuxi Li
8. Matthew Riemer
9. Abhishek Gupta
10. Chengshu Li

Papers from Alexander Sasha Vezhnevets's perspective

Ordered by how strongly the researchers Alexander Sasha Vezhnevets 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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

• Grandmaster level in StarCraft II using multi-agent reinforcement learning (2019) — 4,437 citations
• FeUdal Networks for Hierarchical Reinforcement Learning (2017) — 1,079 citations
• StarCraft II: A New Challenge for Reinforcement Learning (2017) — 971 citations
• Strategic Attentive Writer for Learning Macro-Actions (2016) — 179 citations
• Options as responses: Grounding behavioural hierarchies in multi-agent RL (2019) — 18 citations