Tomohide Masuda

Computational Theory and Mathematics · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 26 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 35 citations into it; #10,079 of 12,182 by trust here, #5,239 by citations
• Molecular Biology — 30 citations into it; #12,847 of 17,773 by trust here, #12,465 by citations
• Computational Chemistry & Drug Discovery — 19 citations into it
• Materials Chemistry — 19 citations into it
• Artificial Intelligence — 17 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Generative Models — 13 citations into it

Whom Tomohide Masuda trusts overall

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

1. Diederik P. Kingma
2. David Weininger
3. Benjamín Sánchez-Lengeling
4. Geoffrey E. Hinton
5. Alec Radford
6. Esben Jannik Bjerrum
7. Alex Krizhevsky
8. Gabriel Lima Guimaraes
9. Jimmy Ba
10. Matt J. Kusner

Who trusts Tomohide Masuda overall

1. Shitong Luo
2. Hannes Stärk
3. Yuanqi Du
4. Octavian-Eugen Ganea

Papers from Tomohide Masuda's perspective

Ordered by how strongly the researchers Tomohide Masuda 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. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• GNINA 1.0: molecular docking with deep learning (2021) — 765 citations
• Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models (2020) — 61 citations
• Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design (2020) — 242 citations