Hiroshi Mamitsuka

Molecular Biology · 1998–2018

A researcher in the Science corpus: 7 papers; trusts 44 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Molecular Biology — 59 citations into it
• Computational Theory and Mathematics — 35 citations into it
• Artificial Intelligence — 29 citations into it; #32,646 of 35,345 by trust here, #21,878 by citations
• Computer Vision and Pattern Recognition — 15 citations into it
• Computational Biology & Bioinformatics — 11 citations into it
• Information Systems — 11 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Natural Language Processing — 7 citations into it

Whom Hiroshi Mamitsuka trusts overall

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

1. Sam T. Roweis
2. Lawrence K. Saul
3. Chih-Jen Lin
4. David Ruppert
5. Mehmet Gönen
6. Yoshihiro Yamanishi
7. Geoffrey E. Hinton
8. Kevin Bleakley
9. Diederik P. Kingma
10. Lihi Zelnik-Manor

Who trusts Hiroshi Mamitsuka overall

1. Burr Settles
2. Nicholas A. Roy
3. Sheila Tejada
4. Hongwei Wang
5. Claudio Persello
6. Zhi-Hua Zhou
7. Devis Tuia
8. Andrew McCallum
9. Prem Melville
10. Daniel Beck

Papers from Hiroshi Mamitsuka's perspective

Ordered by how strongly the researchers Hiroshi Mamitsuka trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Similarity-based machine learning methods for predicting drug-target interactions: a brief review (2014) — 384 citations
• Efficiently finding genome-wide three-way gene interactions from transcript- and genotype-data (2009) — 25 citations
• GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank (2017) — 174 citations
• AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification (2018) — 303 citations
• Collaborative matrix factorization with multiple similarities for predicting drug-target interactions (2013) — 328 citations
• Manifold-based Similarity Adaptation for Label Propagation (2013) — 72 citations
• Query Learning Strategies Using Boosting and Bagging (1998)