Kohei Hayashi

Artificial Intelligence · 2016

A researcher in the Science corpus: 2 papers; trusts 20 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 12 citations into it; #7,927 of 35,345 by trust here, #19,873 by citations
• Safety, Robustness & Interpretability — 7 citations into it; #668 of 2,251 by trust here, #1,907 by citations

Whom Kohei Hayashi trusts overall

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

1. Jerome H. Friedman
2. Tianqi Chen
3. Houtao Deng
4. Leo Breiman
5. Been Kim
6. Niko Speybroeck
7. Hirotugu Akaike
8. Michael I. Jordan
9. Carlos Guestrin
10. Trevor J. Hastie

Who trusts Kohei Hayashi overall

1. Atoosa Kasirzadeh
2. Riccardo Guidotti
3. Giulia Vilone
4. Nadia Burkart
5. Houtao Deng
6. Thibault Laugel
7. Dino Pedreschi
8. Dang Lien Minh
9. Anna Monreale
10. Longo Luca

Papers from Kohei Hayashi's perspective

Ordered by how strongly the researchers Kohei Hayashi 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. Gradient-based learning applied to document recognition
4. Learning Multiple Layers of Features from Tiny Images
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Random Forests
7. ImageNet: A large-scale hierarchical image database
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach (2016) — 100 citations
• Making Tree Ensembles Interpretable (2016) — 73 citations