Ronghui You

Molecular Biology · 2017–2019

A researcher in the Science corpus: 5 papers; trusts 8 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Molecular Biology — 53 citations into it
• Artificial Intelligence — 20 citations into it; #32,728 of 35,345 by trust here, #31,361 by citations
• Computational Theory and Mathematics — 16 citations into it
• Natural Language Processing — 7 citations into it
• Materials Chemistry — 6 citations into it
• Information Systems — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Ronghui You trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Arkaitz Zubiaga
5. Alex Krizhevsky
6. Ilya Sutskever
7. Jimmy Ba
8. Matthew D. Zeiler
9. Nitish Srivastava
10. Yann LeCun

Who trusts Ronghui You overall

1. Derya Soydaner

Papers from Ronghui You's perspective

Ordered by how strongly the researchers Ronghui You 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. 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. Learning Multiple Layers of Features from Tiny Images
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens (2019) — 434 citations
• DeepDock: Enhancing Ligand-protein Interaction Prediction by a Combination of Ligand and Structure Information (2019) — 41 citations
• GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank (2017) — 174 citations
• DeepText2GO: Improving large-scale protein function prediction with deep semantic text representation. (2018) — 29 citations
• AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification (2018) — 303 citations