Jiayu Yao

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 4 papers; trusts 74 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it; #25,934 of 35,345 by trust here, #15,866 by citations
• Probabilistic & Bayesian Methods — 38 citations into it
• Reinforcement Learning — 22 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Optimization & Training — 18 citations into it
• Statistics and Probability — 17 citations into it
• Efficiency & Compression — 14 citations into it
• Management Science and Operations Research — 9 citations into it

Whom Jiayu Yao trusts overall

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

1. Diederik P. Kingma
2. Max Welling
3. Ronald J. Williams
4. Radford M. Neal
5. Geoffrey E. Hinton
6. Richard S. Sutton
7. Christos Louizos
8. José Miguel Hernández-Lobato
9. Yann LeCun
10. Finale Doshi-Velez

Who trusts Jiayu Yao overall

1. Daniel Hsu
2. Danijar Hafner
3. Roberta Raileanu
4. Soumya Ghosh
5. Tameem Adel
6. Maxwell Goldstein

Papers from Jiayu Yao's perspective

Ordered by how strongly the researchers Jiayu Yao 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Evaluating Reinforcement Learning Algorithms in Observational Health Settings (2018) — 151 citations
• Model Selection in Bayesian Neural Networks via Horseshoe Priors (2017) — 133 citations
• Direct Policy Transfer via Hidden Parameter Markov Decision Processes (2018)
• Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors (2018) — 85 citations