Toshihiro Kamishima
Artificial Intelligence · 2011–2020
A researcher in the Science corpus: 6 papers; trusts 25 researchers and is trusted by 61 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 53 citations into it; #3,405 of 35,345 by trust here, #3,937 by citations
- Information Systems — 29 citations into it
- AI Ethics, Fairness & Society — 23 citations into it; #65 of 494 by trust here, #63 by citations
- Management Science and Operations Research — 16 citations into it
- Safety Research — 16 citations into it; #138 of 5,829 by trust here, #188 by citations
- Safety, Robustness & Interpretability — 15 citations into it; #513 of 2,251 by trust here, #570 by citations
- Sociology and Political Science — 14 citations into it; #2,514 of 22,756 by trust here, #2,108 by citations
- Marketing — 11 citations into it
Whom Toshihiro Kamishima trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Toon Calders
- Salvatore Ruggieri
- Alexander Strehl
- Dino Pedreschi
- Yehuda Koren
- Arthur U. Asuncion
- Richard S. Sutton
- Charu C. Aggarwal
- Ruslan Salakhutdinov
- Franco Turini
Who trusts Toshihiro Kamishima overall
- Indrė Žliobaitė
- Lu Zhang
- Muhammad Bilal Zafar
- Luca Oneto
- Michael Feldman
- Benjamin Fish
- Kazuto Fukuchi
- Ninareh Mehrabi
- Dana Pessach
- Xintao Wu
Papers from Toshihiro Kamishima's perspective
Ordered by how strongly the researchers Toshihiro Kamishima trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
- 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
- Fairness-Aware Classifier with Prejudice Remover Regularizer (2012) — 896 citations
- Multistakeholder recommendation: Survey and research directions (2020) — 301 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
- Prediction with Model-Based Neutrality (2013) — 41 citations