Ying Zhao

Artificial Intelligence · Data mining and knowledge discovery · 2001–2010

A researcher in the Science corpus: 5 papers; trusts 14 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 16 citations into it; #6,199 of 35,345 by trust here, #10,207 by citations
• Information Systems — 9 citations into it
• Model Architectures — 9 citations into it
• Natural Language Processing — 9 citations into it
• Computational Theory and Mathematics — 6 citations into it

Whom Ying Zhao trusts overall

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

1. Inderjit S. Dhillon
2. Daniel Boley
3. George Karypis
4. Doug Beeferman
5. Anil K. Jain
6. Dharmendra S. Modha
7. Shigeo Abe
8. Hongyuan Zha
9. Michael S. Steinbach
10. Adam L. Berger

Who trusts Ying Zhao overall

1. Anna-Lan Huang
2. Ramiz M. Aliguliyev
3. Igor Jauk
4. George Karypis
5. Xingcheng Ran
6. Gema Bello Orgaz
7. John A. Bullinaria
8. Chris H. Q. Ding
9. Jure Leskovec
10. Jianwen Zhang

Papers from Ying Zhao's perspective

Ordered by how strongly the researchers Ying Zhao 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Long Short-Term Memory
4. ImageNet classification with deep convolutional neural networks
5. Attention is All you Need (Neural Information Processing Systems)
6. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
7. Gradient-based learning applied to document recognition
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep Belief Networks (2010) — 380 citations
• Hierarchical Clustering Algorithms for Document Datasets (2005) — 734 citations
• Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering (2004) — 691 citations
• Evaluation of hierarchical clustering algorithms for document datasets (2002) — 718 citations
• Criterion Functions for Document Clustering ∗ Experiments and Analysis (2001)