Wei Hu

Artificial Intelligence · International Joint Conference on Artificial Intelligence · 2005–2020

A researcher in the Science corpus: 7 papers; trusts 117 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 130 citations into it; #11,336 of 35,345 by trust here, #15,236 by citations
• Management Science and Operations Research — 50 citations into it; #4,292 of 11,946 by trust here, #3,825 by citations
• Graph Learning — 48 citations into it
• Representation & Self-Supervised Learning — 31 citations into it
• Natural Language Processing — 18 citations into it; #4,625 of 5,688 by trust here, #5,296 by citations
• Knowledge Graphs & Representation — 17 citations into it
• Molecular Biology — 17 citations into it
• Statistical and Nonlinear Physics — 15 citations into it

Whom Wei Hu trusts overall

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

1. Yankai Lin
2. Tomas Mikolov
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Antoine Bordes
6. Diederik P. Kingma
7. Ilya Sutskever
8. Kaiming He
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Wei Hu overall

1. Zequn Sun
2. Simon Lacoste-Julien
3. Fabian M. Suchanek
4. Lingbing Guo
5. Lorena Otero-Cerdeira
6. Shaoxiong Ji
7. Yixin Cao
8. Yuting Wu
9. Konstantina Palla
10. Lingfeng Zhong

Papers from Wei Hu's perspective

Ordered by how strongly the researchers Wei Hu 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Cross-Lingual Entity Alignment via Joint Attribute-Preserving Embedding (2017) — 520 citations
• Bootstrapping Entity Alignment with Knowledge Graph Embedding (2018) — 597 citations
• A benchmarking study of embedding-based entity alignment for knowledge graphs (2020) — 313 citations
• Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs (2019) — 308 citations
• Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation (2019) — 391 citations
• A self-training approach for resolving object coreference on the semantic web (2011) — 123 citations
• GMO: A Graph Matching for Ontologies (2005) — 137 citations