Makoto Yamada

Artificial Intelligence · 2011–2020

A researcher in the Science corpus: 7 papers; trusts 225 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 103 citations into it; #23,307 of 35,345 by trust here, #10,798 by citations
• Computer Vision and Pattern Recognition — 60 citations into it; #19,994 of 21,575 by trust here, #9,305 by citations
• Statistics and Probability — 34 citations into it
• Model Architectures — 21 citations into it; #2,066 of 2,278 by trust here, #1,794 by citations
• Optimization & Training — 21 citations into it
• Neural Architecture Search & AutoML — 17 citations into it
• Statistical Learning Theory — 15 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it

Whom Makoto Yamada trusts overall

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

1. Shun-ichi Amari
2. Bernhard Schölkopf
3. Masashi Sugiyama
4. Geoffrey E. Hinton
5. Arthur Gretton
6. Francis R. Bach
7. Alex Smola
8. Yoshua Bengio
9. Kenji Fukumizu
10. Alex Krizhevsky

Who trusts Makoto Yamada overall

1. Hao Yuan
2. Praneeth Vepakomma
3. Kosuke Yoshida
4. Bo Liu
5. Haiyang Yu
6. Qiang Huang
7. Olivier Goudet
8. Shuhao Cao
9. Naoya Takeishi
10. Chetan Tonde

Papers from Makoto Yamada's perspective

Ordered by how strongly the researchers Makoto Yamada 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. 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. 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

• Transformer Dissection: An Unified Understanding for Transformer’s Attention via the Lens of Kernel (2019) — 333 citations
• High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso (2012) — 344 citations
• Beyond Ranking: Optimizing Whole-Page Presentation (2016) — 86 citations
• Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search (2020) — 43 citations
• GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks (2020) — 484 citations
• Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation (2012) — 489 citations
• Sufficient Component Analysis for Supervised Dimension Reduction (2011)