Michael Laskin

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 5 papers; trusts 466 researchers and is trusted by 353 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 200 citations into it; #2,711 of 35,345 by trust here, #3,978 by citations
• Reinforcement Learning — 172 citations into it; #342 of 2,242 by trust here, #418 by citations
• Computer Vision and Pattern Recognition — 100 citations into it; #2,226 of 21,575 by trust here, #4,887 by citations
• Control and Systems Engineering — 44 citations into it
• Representation & Self-Supervised Learning — 42 citations into it; #566 of 3,145 by trust here, #900 by citations
• Evaluation & Benchmarks — 24 citations into it
• Management Science and Operations Research — 23 citations into it
• Robotics & Control — 18 citations into it

Whom Michael Laskin trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Richard S. Sutton
5. Kaiming He
6. Yann LeCun
7. Yoshua Bengio
8. Ilya Sutskever
9. Sergey Levine
10. Jimmy Ba

Who trusts Michael Laskin overall

1. Denis Yarats
2. Roberta Raileanu
3. Benjamin Eysenbach
4. Rishabh Agarwal
5. Sudeep Dasari
6. Masashi Okada
7. Rob Fergus
8. Sergey Levine
9. Abhinav Kumar Gupta
10. Kaiyang Zhou

Papers from Michael Laskin's perspective

Ordered by how strongly the researchers Michael Laskin 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• CURL: Contrastive Unsupervised Representations for Reinforcement Learning (2020) — 1,318 citations
• Reinforcement Learning with Augmented Data (2020) — 798 citations
• Decision Transformer: Reinforcement Learning via Sequence Modeling (2021) — 2,391 citations
• Decoupling Representation Learning from Reinforcement Learning (2020) — 402 citations
• A Framework for Efficient Robotic Manipulation (2020) — 90 citations