Masashi Tsubaki

Artificial Intelligence · 2018

A researcher in the Science corpus: 2 papers; trusts 35 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 23 citations into it; #29,094 of 35,345 by trust here, #31,361 by citations
• Computational Theory and Mathematics — 23 citations into it; #8,817 of 12,182 by trust here, #5,954 by citations
• Molecular Biology — 21 citations into it; #10,746 of 17,773 by trust here, #13,284 by citations
• Graph Learning — 15 citations into it
• Statistical and Nonlinear Physics — 11 citations into it; #7,953 of 10,127 by trust here, #8,564 by citations
• Computer Vision and Pattern Recognition — 10 citations into it
• Materials Chemistry — 9 citations into it
• Model Architectures — 9 citations into it

Whom Masashi Tsubaki trusts overall

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

1. Diederik P. Kingma
2. Yoon Kim
3. Mehmet Gönen
4. Geoffrey E. Hinton
5. Jimmy Ba
6. Yoshua Bengio
7. Dzmitry Bahdanau
8. Kaiming He
9. Alex Krizhevsky
10. Ilya Sutskever

Who trusts Masashi Tsubaki overall

1. Wen Torng
2. Shuangjia Zheng
3. Pedro Hermosilla
4. Russ B. Altman
5. Hoang NT
6. Yongjian Li
7. Hideyuki Shimizu

Papers from Masashi Tsubaki's perspective

Ordered by how strongly the researchers Masashi Tsubaki 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. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Compound‐protein interaction prediction with end‐to‐end learning of neural networks for graphs and sequences (2018) — 630 citations
• Mean-field theory of graph neural networks in graph partitioning (2018) — 61 citations