Shotaro Akaho

Artificial Intelligence · 2005–2021

A researcher in the Science corpus: 8 papers; trusts 74 researchers and is trusted by 131 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 81 citations into it; #1,680 of 35,345 by trust here, #3,072 by citations
• Optimization & Training — 26 citations into it; #769 of 2,601 by trust here, #787 by citations
• Computer Vision and Pattern Recognition — 22 citations into it; #3,154 of 21,575 by trust here, #8,612 by citations
• Scaling & Learning Theory — 22 citations into it; #176 of 428 by trust here, #303 by citations
• Statistical Learning Theory — 14 citations into it; #571 of 1,038 by trust here, #602 by citations
• AI Ethics, Fairness & Society — 13 citations into it; #64 of 494 by trust here, #63 by citations
• Statistical and Nonlinear Physics — 13 citations into it; #2,803 of 10,127 by trust here, #3,979 by citations
• Information Systems — 11 citations into it

Whom Shotaro Akaho trusts overall

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

1. Vladimir Naumovich Vapnik
2. Shun-ichi Amari
3. Jeffrey Pennington
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Greg Yang
7. Yann LeCun
8. Sam Schoenholz
9. Alex Krizhevsky
10. L. Eon Bottou

Who trusts Shotaro Akaho overall

1. Weiran Wang
2. David Roi Hardoon
3. Sung Ju Hwang
4. Changxing Ding
5. Aviv Eisenschtat
6. Guoqiang Mao
7. Dacheng Tao
8. Lu Zhang
9. Wenzhong Guo
10. Indrė Žliobaitė

Papers from Shotaro Akaho's perspective

Ordered by how strongly the researchers Shotaro Akaho 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Fairness-aware Learning through Regularization Approach (2011) — 464 citations
• A kernel method for canonical correlation analysis (2006) — 563 citations
• Universal statistics of Fisher information in deep neural networks: mean field approach (2018) — 187 citations
• Fairness-Aware Classifier with Prejudice Remover Regularizer (2012) — 896 citations
• Learning curves for continual learning in neural networks: Self-knowledge transfer and forgetting (2021) — 16 citations
• An Application of Inverse Reinforcement Learning to Medical Records of Diabetes Treatment (2013) — 24 citations
• Efficiency Improvement of Neutrality-Enhanced Recommendation (2013) — 41 citations
• Learning algorithms utilizing quasi-geodesic flows on the Stiefel manifold (2005) — 182 citations