Kai Yu

Representation & Self-Supervised Learning · with Tresp · 2004–2007

A researcher in the Science corpus: 5 papers; trusts 21 researchers and is trusted by 52 researchers here, as inferred from the citation graph.

Subfields

• Representation & Self-Supervised Learning — 20 citations into it
• Artificial Intelligence — 13 citations into it; #4,253 of 35,345 by trust here, #6,422 by citations
• Statistical Learning Theory — 9 citations into it
• Natural Language Processing — 5 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it; #449 of 1,525 by trust here, #409 by citations

Whom Kai Yu trusts overall

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

1. David John Cameron MacKay
2. Thorsten Joachims
3. Theodoros Evgeniou
4. Bart J. Bakker
5. Neil D. Lawrence
6. J. Breese
7. Zoubin Ghahramani
8. James C. Bezdek
9. Sam T. Roweis
10. Michael I. Jordan

Who trusts Kai Yu overall

1. Yu Zhang
2. Hal Daumé Iii
3. Dit-Yan Yeung
4. Jianpeng Xu
5. Qiang Yang
6. Jianhui Chen
7. Xilu Wang
8. Shipeng Yu
9. Gianluigi Pillonetto
10. Jian Zhang

Papers from Kai Yu's perspective

Ordered by how strongly the researchers Kai Yu 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. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
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

• Learning Gaussian processes from multiple tasks (2005) — 441 citations
• Supervised probabilistic principal component analysis (2006) — 182 citations
• Active learning via transductive experimental design (2006) — 388 citations
• Robust multi-task learning with t-processes (2007) — 103 citations
• Learning Gaussian Process Kernels via Hierarchical Bayes (2004) — 197 citations