Naonori Ueda

Artificial Intelligence · 1998–2018

A researcher in the Science corpus: 10 papers; trusts 99 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 86 citations into it; #4,333 of 35,345 by trust here, #16,771 by citations
• Computer Vision and Pattern Recognition — 47 citations into it; #4,808 of 21,575 by trust here, #11,083 by citations
• Probabilistic & Bayesian Methods — 30 citations into it
• Representation & Self-Supervised Learning — 29 citations into it
• Signal Processing — 22 citations into it
• Optimization & Training — 18 citations into it
• Efficiency & Compression — 12 citations into it
• Safety, Robustness & Interpretability — 12 citations into it

Whom Naonori Ueda trusts overall

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

1. David John Cameron MacKay
2. Arthur P. Dempster
3. Zoubin Ghahramani
4. Geoffrey E. Hinton
5. Thorsten Joachims
6. Yuhai Wu
7. Hirotugu Akaike
8. Anders Krogh
9. Kazumi Saito
10. Lars Kai Hansen

Who trusts Naonori Ueda overall

1. Zhi-Hua Zhou
2. Min-Ling Zhang
3. J. Urgen Schmidhuber
4. Charles M. Bishop
5. Geoffrey E. Hinton
6. Mohammad S. Sorower
7. Dan Claudiu Ciresan
8. Giorgio Fumera
9. Zoubin Ghahramani
10. Yoshua Bengio

Papers from Naonori Ueda's perspective

Ordered by how strongly the researchers Naonori Ueda 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
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

• Optimal Linear Combination of Neural Networks for Improving Classification Performance (2000) — 254 citations
• Bayesian model search for mixture models based on optimizing variational bounds (2002) — 161 citations
• Few-shot learning of neural networks from scratch by pseudo example optimization (2018) — 55 citations
• Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition (2012) — 80 citations
• Imitation networks: Few-shot learning of neural networks from scratch (2018) — 6 citations
• Large-Scale Personalized Human Activity Recognition Using Online Multitask Learning (2013) — 60 citations
• Fast approximate similarity search based on degree-reduced neighborhood graphs (2011) — 44 citations
• Learning Systems of Concepts with an Infinite Relational Model (2006) — 793 citations
• Parametric Mixture Models for Multi-Labeled Text (2002) — 384 citations
• SMEM Algorithm for Mixture Models (1998)