Kazuto Fukuchi

Artificial Intelligence · 2013–2019

A researcher in the Science corpus: 4 papers; trusts 40 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it; #19,631 of 35,345 by trust here, #27,551 by citations
• Safety, Robustness & Interpretability — 14 citations into it
• Safety Research — 11 citations into it; #2,370 of 5,829 by trust here, #4,191 by citations
• Sociology and Political Science — 11 citations into it
• Statistics and Probability — 11 citations into it
• AI Ethics, Fairness & Society — 10 citations into it
• Privacy & Security — 7 citations into it

Whom Kazuto Fukuchi trusts overall

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

1. Toon Calders
2. Cynthia Dwork
3. Faisal Kamiran
4. Toshihiro Kamishima
5. Oded Goldreich
6. Salvatore Ruggieri
7. Dino Pedreschi
8. Moritz Hardt
9. Sicco Verwer
10. Richard S. Zemel

Who trusts Kazuto Fukuchi overall

1. Indrė Žliobaitė
2. Luca Oneto
3. Silvia Chiappa
4. Robert C. Williamson
5. Aditya Krishna Menon
6. Simon Caton
7. Virgile Landeiro

Papers from Kazuto Fukuchi's perspective

Ordered by how strongly the researchers Kazuto Fukuchi 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
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

• Pretending Fair Decisions via Stealthily Biased Sampling (2019) — 3 citations
• Differentially Private Chi-squared Test by Unit Circle Mechanism (2017) — 23 citations
• Fairness-Aware Learning with Restriction of Universal Dependency using f-Divergences (2015)
• Prediction with Model-Based Neutrality (2013) — 41 citations