Eric Liang

Artificial Intelligence · 2014–2019

A researcher in the Science corpus: 8 papers; trusts 82 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 108 citations into it; #23,325 of 35,345 by trust here, #8,365 by citations
• Reinforcement Learning — 66 citations into it; #2,219 of 2,242 by trust here, #773 by citations
• Computer Vision and Pattern Recognition — 52 citations into it; #14,136 of 21,575 by trust here, #8,952 by citations
• Optimization & Training — 44 citations into it
• Computer Networks and Communications — 28 citations into it
• ML Systems & Hardware — 24 citations into it; #372 of 575 by trust here, #202 by citations
• Information Systems — 20 citations into it
• Model Architectures — 20 citations into it

Whom Eric Liang trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Ronald J. Williams
4. Yoshua Bengio
5. Kaiming He
6. Ilya Sutskever
7. Diederik P. Kingma
8. Yann LeCun
9. Richard S. Sutton
10. John Schulman

Who trusts Eric Liang overall

1. Praveen Palanisamy
2. Yuxi Li
3. Ekin Dogus Cubuk
4. Matthew J. Hausknecht
5. Aleksei Petrenko
6. Barret Zoph
7. Gaurav Menghani
8. Arber Zela
9. Erik Wijmans
10. Cathy Wu

Papers from Eric Liang's perspective

Ordered by how strongly the researchers Eric Liang 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. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules (2019) — 443 citations
• Ray: A Distributed Framework for Emerging AI Applications (2017) — 1,702 citations
• Tune: A Research Platform for Distributed Model Selection and Training (2018) — 1,129 citations
• Ray RLLib: A Composable and Scalable Reinforcement Learning Library (2017) — 181 citations
• Reinforcement (2018)
• Benchmarks for reinforcement learning in mixed-autonomy traffic (2018) — 178 citations
• RLlib: Abstractions for Distributed Reinforcement Learning (2017) — 1,043 citations
• The bilingual advantage in phonetic learning* (2014) — 99 citations