Takafumi Kanamori

Artificial Intelligence · 2008–2012

A researcher in the Science corpus: 7 papers; trusts 68 researchers and is trusted by 66 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it; #6,869 of 35,345 by trust here, #12,761 by citations
• Statistics and Probability — 59 citations into it; #1,340 of 12,932 by trust here, #1,860 by citations
• Statistical Learning Theory — 34 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it; #20,978 of 21,575 by trust here, #19,766 by citations
• Representation & Self-Supervised Learning — 19 citations into it
• Control and Systems Engineering — 17 citations into it
• Optimization & Training — 17 citations into it
• Probabilistic & Bayesian Methods — 15 citations into it; #319 of 1,525 by trust here, #443 by citations

Whom Takafumi Kanamori trusts overall

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

1. Masashi Sugiyama
2. Robert Tibshirani
3. Ingo Steinwart
4. Alex Smola
5. Trevor J. Hastie
6. Bernhard Schölkopf
7. Huaiyu Zhu
8. Lotfi A. Zadeh
9. Peter M. Williams
10. Arthur Gretton

Who trusts Takafumi Kanamori overall

1. Masashi Sugiyama
2. Jiaxin Shi
3. Balaji Lakshminarayanan
4. Ferenc Huszár
5. Song Liu
6. Jun Zhu
7. Wouter M. Kouw
8. Hirotaka Hachiya
9. Qiang Yang
10. Ofir Nachum

Papers from Takafumi Kanamori's perspective

Ordered by how strongly the researchers Takafumi Kanamori 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Density Ratio Estimation in Machine Learning (2012) — 696 citations
• Least-squares two-sample test (2011) — 45 citations
• Density-ratio matching under the Bregman divergence: a unified framework of density-ratio estimation (2012) — 228 citations
• A Least-squares Approach to Direct Importance Estimation (2009) — 611 citations
• Machine Learning in Non-Stationary Environments - Introduction to Covariate Shift Adaptation (2012) — 434 citations
• Conditional Density Estimation via Least-Squares Density Ratio Estimation (2010) — 72 citations
• Robust Boosting Algorithm Against Mislabeling in Multiclass Problems (2008) — 26 citations