Xingquan Zhu

Artificial Intelligence · Artificial Intelligence Review · 2003–2020

A researcher in the Science corpus: 19 papers; trusts 246 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 201 citations into it; #10,495 of 35,345 by trust here, #6,796 by citations
• Statistical and Nonlinear Physics — 84 citations into it; #4,400 of 10,127 by trust here, #2,213 by citations
• Computer Vision and Pattern Recognition — 72 citations into it
• Graph Learning — 70 citations into it
• Representation & Self-Supervised Learning — 70 citations into it
• Information Systems — 43 citations into it; #5,019 of 8,771 by trust here, #2,912 by citations
• Signal Processing — 29 citations into it; #6,884 of 7,572 by trust here, #5,829 by citations
• Molecular Biology — 26 citations into it; #7,444 of 17,773 by trust here, #8,905 by citations

Whom Xingquan Zhu trusts overall

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

1. J. Ross Quinlan
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Yann LeCun
5. Alex Krizhevsky
6. Ilya Sutskever
7. Carla E. Brodley
8. Tomas Mikolov
9. Diederik P. Kingma
10. Ruslan Salakhutdinov

Who trusts Xingquan Zhu overall

1. Shirui Pan
2. David F. Nettleton
3. Albert Orriols-Puig
4. Haitao Liu
5. Qinxue Meng
6. Jacob Goldberger
7. Octavio Loyola-González
8. Jos Akkermans
9. Ruiqi Hu
10. Kamran Shaukat

Papers from Xingquan Zhu's perspective

Ordered by how strongly the researchers Xingquan Zhu 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Class Noise vs. Attribute Noise: A Quantitative Study (2003) — 918 citations
• Network Representation Learning: A Survey (2017) — 693 citations
• MGAE: Marginalized Graph Autoencoder for Graph Clustering (2017) — 521 citations
• Homophily, Structure, and Content Augmented Network Representation Learning (2016) — 104 citations
• Self-adaptive probability estimation for Naive Bayes classification (2013) — 43 citations
• Evolutionary Architecture Search for Graph Neural Networks (2020) — 24 citations
• Dropout vs. batch normalization: an empirical study of their impact to deep learning (2020) — 510 citations
• Multiple Structure-View Learning for Graph Classification (2018) — 75 citations
• User Profile Preserving Social Network Embedding (2017) — 100 citations
• Joint structure feature exploration and regularization for multi-task graph classification (2016) — 55 citations
• Tri-Party Deep Network Representation (2016) — 438 citations
• DATA MINING FOR BIG DATA (2015) — 1,026 citations
• TrGraph: Cross-Network Transfer Learning via Common Signature Subgraphs (2015) — 46 citations
• Multi-graph-view Learning for Graph Classification (2014) — 52 citations
• Transfer Learning across Networks for Collective Classification (2013) — 55 citations
• Machine Learning for Android Malware Detection Using Permission and API Calls (2013) — 351 citations
• Artificial immune system for attribute weighted Naive Bayes classification (2013) — 45 citations
• A survey on instance selection for active learning (2012) — 394 citations
• Enabling Fast Lazy Learning for Data Streams (2011) — 47 citations