Ying Liu

Artificial Intelligence · Pattern Recognition · 2005–2019

A researcher in the Science corpus: 13 papers; trusts 151 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it; #11,913 of 35,345 by trust here, #15,542 by citations
• Statistics and Probability — 41 citations into it
• Computer Vision and Pattern Recognition — 37 citations into it; #19,117 of 21,575 by trust here, #13,542 by citations
• Cognitive Neuroscience — 22 citations into it
• Computational Theory and Mathematics — 19 citations into it; #3,033 of 12,182 by trust here, #6,798 by citations
• Molecular Biology — 19 citations into it
• Genetics — 16 citations into it; #2,088 of 6,854 by trust here, #2,476 by citations
• Information Systems — 14 citations into it; #1,477 of 8,771 by trust here, #5,713 by citations

Whom Ying Liu trusts overall

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

1. Susan A. Murphy
2. Yoshua Bengio
3. James M. Robins
4. Philip W. Lavori
5. David G. Lowe
6. Ying‐Qi Zhao
7. Geoffrey E. Hinton
8. Anastasios A. Tsiatis
9. Alex Krizhevsky
10. Pascal Vincent

Who trusts Ying Liu overall

1. Yu-Chiang Li
2. Mengchi Liu
3. Vincent Shin-Mu Tseng
4. Manfred Reichert
5. Chowdhury Farhan Ahmed
6. M. Mohammadi
7. Martin Solli
8. Junfu Yin
9. Ahmed Awad
10. Jieh-Shan Yeh

Papers from Ying Liu's perspective

Ordered by how strongly the researchers Ying Liu 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• A survey of content-based image retrieval with high-level semantics (2007) — 1,963 citations
• Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens (2018) — 95 citations
• A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets (2005) — 715 citations
• Robust Hybrid Learning for Estimating Personalized Dynamic Treatment Regimens (2016) — 34 citations
• A fast high utility itemsets mining algorithm (2005) — 504 citations
• De Novo Design of Multitarget Ligands with an Iterative Fragment-Growing Strategy (2014) — 54 citations
• A Novel Cloud-Based Framework for the Elderly Healthcare Services Using Digital Twin (2019) — 561 citations
• Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications (2018) — 1,045 citations
• Geological Disaster Recognition on Optical Remote Sensing Images Using Deep Learning (2016) — 142 citations
• Learning Optimal Individualized Treatment Rules from Electronic Health Record Data (2016) — 25 citations
• FaST linear mixed models for genome-wide association studies (2011) — 1,189 citations
• Addiction related alteration in resting-state brain connectivity (2010) — 325 citations
• A static compliance-checking framework for business process models (2007) — 294 citations