Hiroto Saigo

Statistical Learning Theory · 2004–2009

A researcher in the Science corpus: 3 papers; trusts 19 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Statistical Learning Theory — 19 citations into it
• Computational Biology & Bioinformatics — 13 citations into it
• Molecular Biology — 12 citations into it; #13,615 of 17,773 by trust here, #9,851 by citations
• Graph Learning — 8 citations into it
• Optimization & Training — 8 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Hiroto Saigo trusts overall

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

1. Christopher K. I. Williams
2. Christina S. Leslie
3. Robert Tibshirani
4. J. Ross Quinlan
5. William W. Cohen
6. Yoav Freund
7. Hisashi Kashima
8. Hui Zou
9. Xifeng Yan
10. Eleazar Eskin

Who trusts Hiroto Saigo overall

1. Emilio Carrizosa
2. Marco Cuturi
3. Derek Greene
4. Shumin Li
5. Yasser El-Manzalawy
6. Qiwen Dong
7. François Rousseau
8. Jason Aaron Edward Weston
9. Dolores Romero Morales
10. Junjie Chen

Papers from Hiroto Saigo's perspective

Ordered by how strongly the researchers Hiroto Saigo 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. Gradient-based learning applied to document recognition
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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

• Graph kernels for chemical informatics (2005) — 513 citations
• gBoost: a mathematical programming approach to graph classification and regression (2009) — 163 citations
• Protein homology detection using string alignment kernels (2004) — 417 citations