Ben Liao

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 46 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 43 citations into it; #24,569 of 35,345 by trust here, #17,412 by citations
• Computer Vision and Pattern Recognition — 18 citations into it
• Reinforcement Learning — 17 citations into it
• Optimization & Training — 10 citations into it; #2,004 of 2,601 by trust here, #1,804 by citations
• Game & Decision Theory — 5 citations into it
• Model Architectures — 5 citations into it

Whom Ben Liao trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Christian Szegedy
5. Yoshua Bengio
6. X. Zhang
7. Diederik P. Kingma
8. Jia Deng
9. Yann LeCun
10. Alex Graves

Who trusts Ben Liao overall

1. Junnan Li
2. Curtis G. Northcutt
3. Hwanjun Song
4. Jianhao Wang
5. Geoff Pleiss
6. Sheng Liu
7. Chris Dongjoo Kim

Papers from Ben Liao's perspective

Ordered by how strongly the researchers Ben Liao trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels (2019) — 460 citations
• Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models (2020) — 616 citations
• Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning (2020) — 236 citations