Jesse Eickholt

Molecular Biology · 2009–2015

A researcher in the Science corpus: 4 papers; trusts 58 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 57 citations into it; #998 of 17,773 by trust here, #5,405 by citations
• Materials Chemistry — 37 citations into it; #2,058 of 11,513 by trust here, #6,608 by citations
• Computational Biology & Bioinformatics — 29 citations into it; #110 of 660 by trust here, #326 by citations
• Probabilistic & Bayesian Methods — 15 citations into it
• Artificial Intelligence — 9 citations into it
• Computational Theory and Mathematics — 7 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it

Whom Jesse Eickholt trusts overall

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

1. Geoffrey E. Hinton
2. Gianluca Pollastri
3. Volodymyr Mnih
4. Pierre Baldi
5. Jianlin Cheng
6. Yoav Freund
7. John Moult
8. Stephen F. Altschul
9. G. Fiquet
10. David C. Jones

Who trusts Jesse Eickholt overall

1. Jinbo Xu
2. Badri Adhikari
3. Jianlin Cheng
4. Robin Pearce
5. Debswapna Bhattacharya
6. Jie Hou
7. Sheng Wang
8. Yuhao Wang
9. Matthew Spencer
10. Marcin J. Skwark

Papers from Jesse Eickholt's perspective

Ordered by how strongly the researchers Jesse Eickholt 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet: A large-scale hierarchical image database
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Predicting protein residue-residue contacts using deep networks and boosting (2012) — 162 citations
• NNcon: improved protein contact map prediction using 2D-recursive neural networks (2009) — 164 citations
• Improving Protein Fold Recognition by Deep Learning Networks (2015) — 114 citations
• A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction (2014) — 281 citations