Motoaki Kawanabe

Computer Vision and Pattern Recognition · 2003–2021

A researcher in the Science corpus: 12 papers; trusts 350 researchers and is trusted by 57 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 106 citations into it; #10,846 of 21,575 by trust here, #8,021 by citations
• Artificial Intelligence — 103 citations into it; #5,406 of 35,345 by trust here, #3,551 by citations
• Statistical Learning Theory — 37 citations into it; #581 of 1,038 by trust here, #273 by citations
• Evaluation & Benchmarks — 27 citations into it
• Cognitive Neuroscience — 24 citations into it; #2,814 of 10,852 by trust here, #3,702 by citations
• Statistics and Probability — 24 citations into it
• Reinforcement Learning — 20 citations into it
• Natural Language Processing — 19 citations into it

Whom Motoaki Kawanabe trusts overall

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

1. Richard S. Sutton
2. David G. Lowe
3. Masashi Sugiyama
4. Bernhard Schölkopf
5. Geoffrey E. Hinton
6. Diederik P. Kingma
7. Benjamin Blankertz
8. Yann LeCun
9. Alex Krizhevsky
10. Yoshua Bengio

Who trusts Motoaki Kawanabe overall

1. Fabien Lotte
2. Wojciech Samek
3. Masashi Sugiyama
4. Grgoire Montavon
5. Aapo Hyvärinen
6. Junfeng Wen
7. K. Müller
8. Nathan Kallus
9. Mukund Sundararajan
10. Been Kim

Papers from Motoaki Kawanabe's perspective

Ordered by how strongly the researchers Motoaki Kawanabe 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• How to Explain Individual Classification Decisions (2009) — 1,191 citations
• ScanQA: 3D Question Answering for Spatial Scene Understanding (2021) — 447 citations
• Direct importance estimation for covariate shift adaptation (2008) — 498 citations
• Machine Learning in Non-Stationary Environments - Introduction to Covariate Shift Adaptation (2012) — 132 citations
• On Taxonomies for Multi-class Image Categorization (2012) — 57 citations
• Enhanced representation and multi-task learning for image annotation (2013) — 23 citations
• Toward Unsupervised Adaptation of LDA for Brain–Computer Interfaces (2011) — 302 citations
• Generalized TD Learning (2011) — 30 citations
• Insights from Classifying Visual Concepts with Multiple Kernel Learning (2011) — 11 citations
• Optimizing Spatial filters for Robust EEG Single-Trial Analysis (2008) — 2,111 citations
• Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation (2007) — 962 citations
• Kernel-Based Nonlinear Blind Source Separation (2003) — 149 citations