Yu Li

Molecular Biology · with Gao · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 149 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 110 citations into it
• Artificial Intelligence — 102 citations into it
• Computer Vision and Pattern Recognition — 62 citations into it
• Model Architectures — 35 citations into it
• Computational Biology & Bioinformatics — 30 citations into it
• Optimization & Training — 22 citations into it
• Natural Language Processing — 18 citations into it
• Representation & Self-Supervised Learning — 18 citations into it

Whom Yu Li trusts overall

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

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

Who trusts Yu Li overall

1. Felipe Kenji Nakano
2. Mathias Lietaert
3. Onur Tasar
4. Zhenzhen Zou
5. Vinod Kumar Kurmi
6. Peiqiu Chen
7. Wei Wei
8. Patrick H. Chen
9. Xilai Li
10. Celine Vens

Papers from Yu Li's perspective

Ordered by how strongly the researchers Yu Li 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep learning in bioinformatics: introduction, application, and perspective in big data era (2019) — 335 citations
• DEEPre: sequence-based enzyme EC number prediction by deep learning (2017) — 268 citations
• RNA Secondary Structure Prediction By Learning Unrolled Algorithms (2020) — 137 citations
• mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning (2019) — 105 citations
• SupportNet: solving catastrophic forgetting in class incremental learning with support data (2018) — 31 citations