Joshua Meier

Molecular Biology · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 56 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 220 citations into it; #4,125 of 17,773 by trust here, #2,612 by citations
• Computational Biology & Bioinformatics — 186 citations into it; #555 of 660 by trust here, #395 by citations
• Artificial Intelligence — 57 citations into it
• Materials Chemistry — 48 citations into it
• Genetics — 40 citations into it
• Representation & Self-Supervised Learning — 33 citations into it; #2,424 of 3,145 by trust here, #790 by citations
• Natural Language Processing — 32 citations into it
• Model Architectures — 28 citations into it

Whom Joshua Meier trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Yann LeCun
4. Diederik P. Kingma
5. Ilya Sutskever
6. Alec Radford
7. Adam J. Riesselman
8. Jacob Devlin
9. Alex Krizhevsky
10. Jimmy Ba

Who trusts Joshua Meier overall

1. Ratul Chowdhury
2. Jeffrey A. Ruffolo
3. Kevin Kaichuang Yang
4. Brian L. Hie
5. Ahmed Elnaggar
6. Michael Heinzinger
7. Nadav Brandes
8. Erik Nijkamp
9. Nazim Bouatta
10. Theodore R. Sumers

Papers from Joshua Meier's perspective

Ordered by how strongly the researchers Joshua Meier 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. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Evaluating Reinforcement Learning Algorithms in Observational Health Settings (2018) — 151 citations
• Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019) — 3,265 citations
• MSA Transformer (2021) — 735 citations
• Energy-based models for atomic-resolution protein conformations (2020) — 61 citations
• Language models enable zero-shot prediction of the effects of mutations on protein function (2021) — 898 citations
• Transformer protein language models are unsupervised structure learners (2020) — 394 citations