Motonobu Kanagawa

Artificial Intelligence · 2013–2018

A researcher in the Science corpus: 5 papers; trusts 88 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 87 citations into it
• Statistics and Probability — 52 citations into it
• Probabilistic & Bayesian Methods — 51 citations into it; #1,004 of 1,525 by trust here, #1,365 by citations
• Statistical Learning Theory — 50 citations into it; #1,015 of 1,038 by trust here, #969 by citations
• Computer Vision and Pattern Recognition — 22 citations into it
• Representation & Self-Supervised Learning — 16 citations into it; #2,325 of 3,145 by trust here, #3,047 by citations
• Statistical and Nonlinear Physics — 15 citations into it
• Aerospace Engineering — 13 citations into it

Whom Motonobu Kanagawa trusts overall

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

1. Arthur Gretton
2. Le Song
3. Kenji Fukumizu
4. Francis R. Bach
5. Alex Smola
6. Bernhard Schölkopf
7. Bharath K. Sriperumbudur
8. Ingo Steinwart
9. Sebastian Thrun
10. Michael I. Jordan

Who trusts Motonobu Kanagawa overall

1. Krikamol Muandet
2. Bernhard Schölkopf
3. Kenji Fukumizu
4. Yu Nishiyama
5. Carl-Johann Simon-Gabriel
6. Bharath K. Sriperumbudur
7. Adam Scibior
8. Amir Hossein Afsharinejad

Papers from Motonobu Kanagawa's perspective

Ordered by how strongly the researchers Motonobu Kanagawa 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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

• Filtering with State-Observation Examples via Kernel Monte Carlo Filter (2013) — 22 citations
• Monte Carlo Filtering Using Kernel Embedding of Distributions (2014) — 14 citations
• Convergence guarantees for kernel-based quadrature rules in misspecified settings (2016) — 57 citations
• Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences (2018) — 431 citations
• Recovering Distributions from Gaussian RKHS Embeddings (2014) — 34 citations