Yu Wang

Molecular Biology · with Dallago · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 64 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 200 citations into it
• Computational Biology & Bioinformatics — 111 citations into it; #552 of 660 by trust here, #376 by citations
• Artificial Intelligence — 66 citations into it
• Materials Chemistry — 56 citations into it
• Natural Language Processing — 40 citations into it
• Representation & Self-Supervised Learning — 33 citations into it; #2,891 of 3,145 by trust here, #1,430 by citations
• Model Architectures — 28 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it

Whom Yu Wang trusts overall

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

1. Burkhard Rost
2. Christopher Sander
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Avner Schlessinger
6. Tomas Mikolov
7. Sheng Wang
8. L. V. D. van der Maaten
9. Yoshua Bengio
10. Sebastian Briesemeister

Who trusts Yu Wang overall

1. Dan Ofer
2. Michael Heinzinger
3. Konstantin Weissenow

Papers from Yu Wang's perspective

Ordered by how strongly the researchers Yu Wang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet classification with deep convolutional neural networks
6. Gradient-based learning applied to document recognition
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet: A large-scale hierarchical image database
10. Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)

Their papers

• ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing (2020) — 1,354 citations
• Modeling aspects of the language of life through transfer-learning protein sequences (2019) — 548 citations
• Modeling the language of life – Deep Learning Protein Sequences (2019) — 48 citations
• ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Learning (2021)