Rhys Heffernan

Molecular Biology · 2014–2018

A researcher in the Science corpus: 8 papers; trusts 18 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 122 citations into it; #2,423 of 17,773 by trust here, #3,835 by citations
• Materials Chemistry — 77 citations into it; #1,626 of 11,513 by trust here, #4,427 by citations
• Artificial Intelligence — 27 citations into it
• Model Architectures — 17 citations into it
• Optimization & Training — 16 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Signal Processing — 8 citations into it
• Natural Language Processing — 7 citations into it

Whom Rhys Heffernan trusts overall

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

1. J. Urgen Schmidhuber
2. Yoshua Bengio
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Sepp Hochreiter
6. Rasmus Berg Palm
7. Yann LeCun
8. Mike Schuster
9. Alex Graves
10. Yuedong Yang

Who trusts Rhys Heffernan overall

1. Jack Hanson
2. Michael Heinzinger
3. Yuedong Yang
4. Chaopeng Shen
5. Yujuan Gao
6. Ahmed Elnaggar
7. Sheng Wang
8. Kuldip K. Paliwal
9. Mirko Torrisi
10. Joe G. Greener

Papers from Rhys Heffernan's perspective

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

Their papers

• Capturing non‐local interactions by long short‐term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility (2017) — 329 citations
• Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto‐encoder deep neural network (2014) — 152 citations
• Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning (2015) — 346 citations
• SPIDER2: A Package to Predict Secondary Structure, Accessible Surface Area, and Main-Chain Torsional Angles by Deep Neural Networks. (2017) — 149 citations
• Sixty-five years of the long march in protein secondary structure prediction: the final stretch? (2016) — 235 citations
• Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins (2016) — 99 citations
• SPIN2: Predicting sequence profiles from protein structures using deep neural networks (2018) — 101 citations
• Detecting Proline and Non-Proline Cis Isomers in Protein Structures from Sequences Using Deep Residual Ensemble Learning (2018) — 14 citations