David R. Kelley

Molecular Biology · 2009–2021

A researcher in the Science corpus: 9 papers; trusts 75 researchers and is trusted by 41 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 208 citations into it; #1,136 of 17,773 by trust here, #1,655 by citations
• Genetics — 55 citations into it
• Artificial Intelligence — 37 citations into it
• Plant Science — 30 citations into it; #739 of 8,232 by trust here, #2,511 by citations
• Ecology — 17 citations into it; #6,066 of 10,717 by trust here, #9,865 by citations
• Computer Vision and Pattern Recognition — 16 citations into it
• Computational Biology & Bioinformatics — 12 citations into it
• Model Architectures — 10 citations into it

Whom David R. Kelley trusts overall

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

1. Yann LeCun
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Jasper Snoek
5. Terence P. Speed
6. Sergey Ioffe
7. Alex Krizhevsky
8. Daniel J. Lizotte
9. Vinod Nair
10. Gary D. Stormo

Who trusts David R. Kelley overall

1. Avanti Shrikumar
2. Guillaume Marçais
3. Hayssam Soueidan
4. Niranjan Nagarajan
5. Rayan Chikhi
6. Wojciech Samek
7. Li Song
8. Tanja Magoc
9. Daniel Quang
10. Marten Boetzer

Papers from David R. Kelley's perspective

Ordered by how strongly the researchers David R. Kelley trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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. Going deeper with convolutions

Their papers

• Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks (2012) — 11,804 citations
• Quake: quality-aware detection and correction of sequencing errors (2010) — 632 citations
• Assemblathon 1: a competitive assessment of de novo short read assembly methods. (2011) — 544 citations
• Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks (2015) — 953 citations
• Clustering metagenomic sequences with interpolated Markov models (2010) — 120 citations
• Effective gene expression prediction from sequence by integrating long-range interactions (2021) — 1,222 citations
• Topological Organization of Multi-chromosomal Regions by Firre (2014) — 596 citations
• Sequential regulatory activity prediction across chromosomes with convolutional neural networks (2017) — 513 citations
• A whole-genome assembly of the domestic cow, Bos taurus (2009) — 1,125 citations