Xu-Ying Liu

Artificial Intelligence · with Zhou · 2006

A researcher in the Science corpus: 3 papers; trusts 7 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 15 citations into it; #7,724 of 35,345 by trust here, #19,439 by citations
• Information Systems — 8 citations into it
• Statistical Learning Theory — 5 citations into it

Whom Xu-Ying Liu trusts overall

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

1. Charles Peter Elkan
2. Pedro M. Domingos
3. Gary M. Weiss
4. Zhi-Hua Zhou
5. Bianca Zadrozny
6. Niko Speybroeck
7. M. Maloof
8. Philip Ka-Fai Chan
9. C. Drummond
10. J. Langford

Who trusts Xu-Ying Liu overall

1. Siong Thye Goh
2. Alberto Fernández
3. K. Ruwani
4. Ningshan Chen
5. Youngkyu Hong
6. Nguyen Thai-Nghe
7. Shuo Wang
8. Zhi-Hua Zhou
9. Haibo He
10. Victoria López

Papers from Xu-Ying Liu's perspective

Ordered by how strongly the researchers Xu-Ying Liu trusts endorse each paper. Same corpus, a different order for every researcher.

1. Gradient-based learning applied to document recognition
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. The Foundations of Cost-Sensitive Learning
5. Long Short-Term Memory
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Cost-sensitive learning by cost-proportionate example weighting
8. Reinforcement Learning: An Introduction
9. Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction (Journal of Artificial Intelligence Research)
10. UCI Repository of machine learning databases

Their papers

• ON MULTI‐CLASS COST‐SENSITIVE LEARNING (2006) — 358 citations
• The Influence of Class Imbalance on Cost-Sensitive Learning: An Empirical Study (2006) — 280 citations
• Ieee Transactions on Knowledge and Data Engineering 1 Training Cost-sensitive Neural Networks with Methods Addressing the Class Imbalance Problem