Di Wu

Molecular Biology · with Xu · 2013–2023

A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 27 citations into it
• Computational Theory and Mathematics — 24 citations into it
• Artificial Intelligence — 14 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it
• Materials Chemistry — 10 citations into it
• Natural Language Processing — 10 citations into it
• Accounting — 7 citations into it; #1,847 of 8,970 by trust here, #4,459 by citations
• Computational Chemistry & Drug Discovery — 7 citations into it

Whom Di Wu trusts overall

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

1. Paul Griffin
2. Ilya Sutskever
3. David Weininger
4. Werner Antweiler
5. Tim Loughran
6. Sanjiv Ranjan Das
7. Geoffrey E. Hinton
8. Shuping Chen
9. Oriol Vinyals
10. Yoshua Bengio

Who trusts Di Wu overall

1. Camila C. Pereira
2. L. Smales
3. Gilberto Marquez-Illescas
4. Khrystyna Bochkay
5. Matthew Gentzkow
6. Thomas Renault
7. Heeyoung Lee
8. Tim Loughran
9. Yue Cao
10. Hannes Stärk

Papers from Di Wu's perspective

Ordered by how strongly the researchers Di 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. Long Short-Term Memory
5. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks (2018) — 441 citations
• A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G (2023) — 345 citations
• Word Power: A New Approach for Content Analysis (2013) — 472 citations