Freyr Sverrisson

Molecular Biology · 2014–2022

A researcher in the Science corpus: 5 papers; trusts 22 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 70 citations into it; #14,416 of 17,773 by trust here, #9,030 by citations
• Materials Chemistry — 29 citations into it; #10,732 of 11,513 by trust here, #10,083 by citations
• Computational Biology & Bioinformatics — 27 citations into it; #621 of 660 by trust here, #462 by citations
• Computational Mechanics — 23 citations into it; #9,668 of 10,001 by trust here, #6,582 by citations
• Computer Vision and Pattern Recognition — 23 citations into it
• Artificial Intelligence — 13 citations into it
• Computational Theory and Mathematics — 13 citations into it; #11,006 of 12,182 by trust here, #2,851 by citations
• Graph Learning — 10 citations into it; #672 of 693 by trust here, #355 by citations

Whom Freyr Sverrisson trusts overall

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

1. Jean Feydy
2. Dina Schneidman-Duhovny
3. Benjamin Charlier
4. Sarah A. Teichmann
5. Matthias Fey
6. Sankar Basu
7. Joan Alexis Glaunès
8. Charles Ruizhongtai Qi
9. Michael M. Bronstein
10. Ruth Nussinov

Who trusts Freyr Sverrisson overall

1. Octavian-Eugen Ganea
2. Zuobai Zhang
3. Bruno E. Correia
4. Jérôme Tubiana
5. Vignesh Ram Somnath
6. Charlotte Bunne
7. Marinka Zitnik
8. Minghao Xu
9. Lion Hirth
10. Arian Jamasb

Papers from Freyr Sverrisson's perspective

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

Their papers

• Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning (2019) — 804 citations
• Fast end-to-end learning on protein surfaces (2020) — 197 citations
• Rosetta FunFolDes – A general framework for the computational design of functional proteins (2018) — 36 citations
• P HYSICS - INFORMED DEEP NEURAL NETWORK FOR RIGID - BODY PROTEIN DOCKING (2022)
• Renewables 2014 : global status report (2014) — 1,505 citations