Xindong Wu

Artificial Intelligence · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2003–2023

A researcher in the Science corpus: 18 papers; trusts 1,393 researchers and is trusted by 60 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 366 citations into it; #14,462 of 35,345 by trust here, #4,080 by citations
• Computer Vision and Pattern Recognition — 305 citations into it; #4,647 of 21,575 by trust here, #1,929 by citations
• Natural Language Processing — 95 citations into it
• Management Science and Operations Research — 42 citations into it
• Representation & Self-Supervised Learning — 42 citations into it
• Information Systems — 40 citations into it; #956 of 8,771 by trust here, #296 by citations
• Large Language Models — 40 citations into it
• Molecular Biology — 36 citations into it

Whom Xindong Wu trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Ross B. Girshick
4. Alex Krizhevsky
5. Yoshua Bengio
6. Yann LeCun
7. Diederik P. Kingma
8. Ilya Sutskever
9. Christian Szegedy
10. Karen Simonyan

Who trusts Xindong Wu overall

1. David Sculley
2. Francisco Herrera
3. Salvador Garcia
4. Isaac Triguero
5. Dacheng Tao
6. S. Y. S. Ng
7. David F. Nettleton
8. Jos Akkermans
9. Emilio Carrizosa
10. Alejandro Peña Ayala

Papers from Xindong Wu's perspective

Ordered by how strongly the researchers Xindong Wu 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. ImageNet classification with deep convolutional neural networks
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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• General Tensor Discriminant Analysis and Gabor Features for Gait Recognition (2007) — 1,189 citations
• Object Detection With Deep Learning: A Review (2018) — 4,776 citations
• Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval (2006) — 960 citations
• Multimodal Graph-Based Reranking for Web Image Search (2012) — 366 citations
• Geometric Mean for Subspace Selection (2009) — 590 citations
• Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search (2013) — 463 citations
• Class Noise vs. Attribute Noise: A Quantitative Study (2003) — 918 citations
• Efficient mining of both positive and negative association rules (2004) — 526 citations
• Unifying Large Language Models and Knowledge Graphs: A Roadmap (2023) — 1,606 citations
• A Comprehensive Survey on Automatic Knowledge Graph Construction (2023) — 347 citations
• Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection (2017) — 318 citations
• Scalable Semi-Supervised Learning by Efficient Anchor Graph Regularization (2016) — 220 citations
• DATA MINING FOR BIG DATA (2015) — 1,026 citations
• Visual Classification by ℓ1-Hypergraph Modeling (2015) — 123 citations
• SMILE: A Similarity-Based Approach for Multiple Instance Learning (2010) — 14 citations
• The Top Ten Algorithms in Data Mining (2009) — 721 citations
• Top 10 algorithms in data mining (2007) — 5,999 citations
• 10 Challenging Problems in Data Mining Research (2006) — 966 citations