Shaun M. Kandathil

Molecular Biology · 2018–2022

A researcher in the Science corpus: 7 papers; trusts 143 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 241 citations into it; #1,272 of 17,773 by trust here, #5,864 by citations
• Computational Biology & Bioinformatics — 142 citations into it; #149 of 660 by trust here, #416 by citations
• Materials Chemistry — 67 citations into it
• Artificial Intelligence — 66 citations into it
• Model Architectures — 39 citations into it; #681 of 2,278 by trust here, #1,433 by citations
• Optimization & Training — 32 citations into it
• Computer Vision and Pattern Recognition — 29 citations into it
• Computational Theory and Mathematics — 22 citations into it

Whom Shaun M. Kandathil trusts overall

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

1. Geoffrey E. Hinton
2. David T. Jones
3. Mohammed Alquraishi
4. Jinbo Xu
5. Yoshua Bengio
6. Alex Krizhevsky
7. Yang Zhang
8. Diederik P. Kingma
9. Sean R. Eddy
10. Sheng Wang

Who trusts Shaun M. Kandathil overall

1. Joe G. Greener
2. David T. Jones
3. Yang Li
4. Robin Pearce
5. Badri Adhikari
6. Wei Zheng
7. Chengxin Zhang
8. Yang Zhang
9. Mirko Torrisi
10. Rui Yin

Papers from Shaun M. Kandathil's perspective

Ordered by how strongly the researchers Shaun M. Kandathil 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. Attention is All you Need (Neural Information Processing Systems)
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features (2018) — 169 citations
• 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
• Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins (2022) — 40 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