Shin Ishii

Artificial Intelligence · 2000–2017

A researcher in the Science corpus: 8 papers; trusts 99 researchers and is trusted by 502 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it; #1,930 of 35,345 by trust here, #2,502 by citations
• Computer Vision and Pattern Recognition — 29 citations into it; #1,974 of 21,575 by trust here, #1,986 by citations
• Optimization & Training — 27 citations into it
• Model Architectures — 25 citations into it
• Reinforcement Learning — 24 citations into it; #1,418 of 2,242 by trust here, #1,728 by citations
• Representation & Self-Supervised Learning — 23 citations into it; #516 of 3,145 by trust here, #478 by citations
• Statistics and Probability — 13 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it

Whom Shin Ishii trusts overall

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

1. Richard S. Sutton
2. Leslie Pack Kaelbling
3. Masa-aki Sato
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Andrew Barto
7. Yoshua Bengio
8. Alex Krizhevsky
9. Michael I. Jordan
10. Andrew W. Moore

Who trusts Shin Ishii overall

1. Kenji Doya
2. Stefan Elfwing
3. Karl J. Friston
4. Nicholas Carlini
5. Matthew D. Hoffman
6. Yoshua Bengio
7. Kevin Gimpel
8. Olivier Capp'e
9. Ekin Dogus Cubuk
10. Quoc V. Le

Papers from Shin Ishii's perspective

Ordered by how strongly the researchers Shin Ishii 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet: A large-scale hierarchical image database
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning (2017) — 3,088 citations
• Distributional Smoothing with Virtual Adversarial Training (2015) — 467 citations
• Control of exploitation-exploration meta-parameter in reinforcement learning (2002) — 225 citations
• Reinforcement Learning for Biped Locomotion (2002) — 38 citations
• A Bayesian missing value estimation method for gene expression profile data (2003) — 790 citations
• Distributional Smoothing by Virtual Adversarial Examples (2015) — 73 citations
• Generalized TD Learning (2011) — 30 citations
• On-line EM Algorithm for the Normalized Gaussian Network (2000) — 311 citations