Jason Liu
Molecular Biology · 2019–2022
A researcher in the Science corpus: 4 papers; trusts 18 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 139 citations into it; #17,553 of 17,773 by trust here, #6,609 by citations
- Computational Biology & Bioinformatics — 97 citations into it
- Artificial Intelligence — 42 citations into it; #23,613 of 35,345 by trust here, #23,077 by citations
- Information Systems — 42 citations into it; #5,252 of 8,771 by trust here, #5,078 by citations
- Program Analysis & Verification — 25 citations into it; #156 of 301 by trust here, #169 by citations
- Software — 23 citations into it
- Natural Language Processing — 17 citations into it
- Signal Processing — 12 citations into it
Whom Jason Liu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Adam J. Riesselman
- Ilya Sutskever
- Badri Adhikari
- Geoffrey E. Hinton
- Roshan Rao
- Yoshua Bengio
- Jianyi Yang
- Diederik P. Kingma
- Tomas Mikolov
- John Ingraham
Who trusts Jason Liu overall
Papers from Jason Liu's perspective
Ordered by how strongly the researchers Jason Liu trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- TypeWriter: neural type prediction with search-based validation (2019) — 135 citations
- MSA Transformer (2021) — 735 citations
- Learning inverse folding from millions of predicted structures (2022) — 618 citations
- Language models enable zero-shot prediction of the effects of mutations on protein function (2021) — 898 citations