Joe G. Greener

Molecular Biology · 2018–2022

A researcher in the Science corpus: 8 papers; trusts 163 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 276 citations into it; #1,824 of 17,773 by trust here, #5,864 by citations
• Computational Biology & Bioinformatics — 161 citations into it; #374 of 660 by trust here, #436 by citations
• Materials Chemistry — 86 citations into it; #5,192 of 11,513 by trust here, #10,845 by citations
• Artificial Intelligence — 71 citations into it
• Model Architectures — 44 citations into it
• Computer Vision and Pattern Recognition — 34 citations into it
• Optimization & Training — 32 citations into it
• Computational Theory and Mathematics — 26 citations into it

Whom Joe G. Greener trusts overall

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

1. David T. Jones
2. Geoffrey E. Hinton
3. Yang Zhang
4. Jinbo Xu
5. Diederik P. Kingma
6. Yoshua Bengio
7. Shaun M. Kandathil
8. Mohammed Alquraishi
9. Alex Krizhevsky
10. Jeffrey Skolnick

Who trusts Joe G. Greener overall

1. Shaun M. Kandathil
2. Robin Pearce
3. J. Trinquier
4. David T. Jones
5. Lewis Moffat
6. Yang Zhang
7. Yang Li
8. Wenhao Gao
9. Mostafa Karimi
10. Wei Zheng

Papers from Joe G. Greener's perspective

Ordered by how strongly the researchers Joe G. Greener 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Prediction of interresidue contacts with DeepMetaPSICOV in CASP13 (2019) — 99 citations
• Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints (2018) — 168 citations
• Design of metalloproteins and novel protein folds using variational autoencoders (2018) — 140 citations
• Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins (2022) — 40 citations
• Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design (2021) — 30 citations
• Recent developments in deep learning applied to protein structure prediction (2019) — 53 citations
• A guide to machine learning for biologists (2021) — 1,713 citations
• Deep learning-based prediction of protein structure using learned representations of multiple sequence alignments (2020) — 13 citations