Shohei Hido

Artificial Intelligence · 2009

A researcher in the Science corpus: 2 papers; trusts 31 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 26 citations into it; #33,199 of 35,345 by trust here, #21,316 by citations
• Statistical Learning Theory — 13 citations into it
• Statistics and Probability — 13 citations into it; #2,489 of 12,932 by trust here, #3,177 by citations
• Representation & Self-Supervised Learning — 8 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Optimization & Training — 6 citations into it

Whom Shohei Hido trusts overall

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

1. Masashi Sugiyama
2. Lotfi A. Zadeh
3. Robert A. Lordo
4. Robert Tibshirani
5. Ingo Steinwart
6. Alex Smola
7. Bernhard Schölkopf
8. Huaiyu Zhu
9. Trevor J. Hastie
10. Katya Scheinberg

Who trusts Shohei Hido overall

1. Masashi Sugiyama
2. Jiaxin Shi
3. Wouter M. Kouw
4. Ferenc Huszár
5. Qiang Yang
6. Jun Zhu
7. Zhanglong Ji
8. Kazuto Fukuchi
9. Hirotaka Hachiya
10. Ishan Misra

Papers from Shohei Hido's perspective

Ordered by how strongly the researchers Shohei Hido 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. ImageNet: A large-scale hierarchical image database
6. Learning Multiple Layers of Features from Tiny Images
7. Reinforcement Learning: An Introduction
8. Attention is All you Need (Neural Information Processing Systems)
9. Long Short-Term Memory
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation (2009) — 173 citations
• A Least-squares Approach to Direct Importance Estimation (2009) — 611 citations