Jack Hanson

Molecular Biology · with Paliwal · 2016–2020

A researcher in the Science corpus: 10 papers; trusts 108 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 277 citations into it; #3,969 of 17,773 by trust here, #4,657 by citations
• Materials Chemistry — 134 citations into it
• Computational Biology & Bioinformatics — 84 citations into it
• Artificial Intelligence — 60 citations into it
• Model Architectures — 60 citations into it
• Optimization & Training — 42 citations into it
• Spectroscopy — 42 citations into it
• Computer Vision and Pattern Recognition — 30 citations into it

Whom Jack Hanson trusts overall

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

1. Sepp Hochreiter
2. Vladimir N Uversky
3. Kuldip K. Paliwal
4. Geoffrey E. Hinton
5. Mike Schuster
6. J. Urgen Schmidhuber
7. Kaiming He
8. Yuhai Wu
9. Radford M. Neal
10. Diederik P. Kingma

Who trusts Jack Hanson overall

1. Kuldip K. Paliwal
2. Carter J. Wilson
3. Yaoqi Zhou
4. Jaswinder Singh
5. Maithra Raghu
6. Wing Yiu Choy
7. Thomas Litfin
8. Shaun M. Kandathil
9. Gang Xu
10. Mikko Karttunen

Papers from Jack Hanson's perspective

Ordered by how strongly the researchers Jack Hanson 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks (2018) — 158 citations
• RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning (2019) — 348 citations
• Sixty-five years of the long march in protein secondary structure prediction: the final stretch? (2016) — 235 citations
• SPIN2: Predicting sequence profiles from protein structures using deep neural networks (2018) — 101 citations
• Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks (2018) — 171 citations
• Accurate Single-Sequence Prediction of Protein Intrinsic Disorder by an Ensemble of Deep Recurrent and Convolutional Architectures (2018) — 78 citations
• Detecting Proline and Non-Proline Cis Isomers in Protein Structures from Sequences Using Deep Residual Ensemble Learning (2018) — 14 citations
• Improving protein disorder prediction by deep bidirectional long short‐term memory recurrent neural networks (2016) — 250 citations
• Critical assessment of protein intrinsic disorder prediction (2020) — 286 citations
• SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning (2019) — 139 citations