Alexandre Galashov

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 5 papers; trusts 116 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 177 citations into it; #29,938 of 35,345 by trust here, #9,369 by citations
• Reinforcement Learning — 129 citations into it
• Computer Vision and Pattern Recognition — 53 citations into it
• Representation & Self-Supervised Learning — 34 citations into it
• Optimization & Training — 29 citations into it
• Computational Theory and Mathematics — 27 citations into it
• Control and Systems Engineering — 27 citations into it
• Management Science and Operations Research — 22 citations into it

Whom Alexandre Galashov trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. John Schulman
4. Alex Krizhevsky
5. Ronald J. Williams
6. Yoshua Bengio
7. E. Todorov
8. Sergey Levine
9. Richard S. Sutton
10. Pieter Abbeel

Who trusts Alexandre Galashov overall

1. Ron Dorfman
2. Wei-Cheng Tseng
3. Emilio Parisotto
4. Dhruva Tirumala
5. Josh Merel
6. Jin-Siang Lin
7. Aviv Tamar

Papers from Alexandre Galashov's perspective

Ordered by how strongly the researchers Alexandre Galashov 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. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Meta reinforcement learning as task inference (2019) — 142 citations
• Information asymmetry in KL-regularized RL (2019) — 107 citations
• Task Agnostic Continual Learning via Meta Learning (2019) — 103 citations
• Exploiting Hierarchy for Learning and Transfer in KL-regularized RL (2019) — 45 citations
• Neural probabilistic motor primitives for humanoid control (2018) — 187 citations