Steven Lin

Artificial Intelligence · with Levine · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 101 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 93 citations into it; #8,076 of 35,345 by trust here, #7,531 by citations
• Reinforcement Learning — 93 citations into it; #1,016 of 2,242 by trust here, #885 by citations
• Computer Vision and Pattern Recognition — 40 citations into it
• Control and Systems Engineering — 32 citations into it; #2,242 of 8,257 by trust here, #891 by citations
• Computational Theory and Mathematics — 19 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Generative Models — 15 citations into it
• Robotics & Control — 14 citations into it

Whom Steven Lin trusts overall

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

1. Richard S. Sutton
2. Diederik P. Kingma
3. Sergey Levine
4. Geoffrey E. Hinton
5. J. Urgen Schmidhuber
6. Yoshua Bengio
7. Pieter Abbeel
8. Alex Krizhevsky
9. E. Todorov
10. Yann LeCun

Who trusts Steven Lin overall

1. Benjamin Eysenbach
2. Corey Lynch
3. Sergey Levine
4. Suraj Nair
5. Francesco Locatello

Papers from Steven Lin's perspective

Ordered by how strongly the researchers Steven Lin 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Auto-Encoding Variational Bayes (International Conference on Learning Representations)

Their papers

• Visual Reinforcement Learning with Imagined Goals (2018) — 603 citations
• Skew-Fit: State-Covering Self-Supervised Reinforcement Learning (2019) — 313 citations
• Planning with Goal-Conditioned Policies (2019) — 255 citations