Leopold Parts
Molecular Biology · 2007–2016
A researcher in the Science corpus: 8 papers; trusts 100 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 126 citations into it; #3,502 of 17,773 by trust here, #3,027 by citations
- Genetics — 56 citations into it; #1,990 of 6,854 by trust here, #1,179 by citations
- Computer Vision and Pattern Recognition — 55 citations into it; #12,218 of 21,575 by trust here, #18,266 by citations
- Artificial Intelligence — 51 citations into it
- Model Architectures — 25 citations into it
- Optimization & Training — 21 citations into it
- Biophysics — 13 citations into it; #225 of 1,196 by trust here, #212 by citations
- Computational Biology & Bioinformatics — 11 citations into it; #330 of 660 by trust here, #326 by citations
Whom Leopold Parts trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Radford M. Neal
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Karen Simonyan
- Yann LeCun
- Alex Graves
- David John Cameron MacKay
- Diederik P. Kingma
Who trusts Leopold Parts overall
- Oliver Stegle
- Christof Angermueller
- Jeffrey T. Leek
- Mufti Mahmud
- Andrey Kan
- Barbara Rakitsch
- Erick Moen
- Kimberly R. Kukurba
- Sarah A. Teichmann
- Ben T Grys
Papers from Leopold Parts's perspective
Ordered by how strongly the researchers Leopold Parts trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Discovery of functional elements in 12 Drosophila genomes using evolutionary signatures (2007) — 657 citations
- Deep learning for computational biology (2016) — 1,263 citations
- A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies (2010) — 486 citations
- Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses (2012) — 958 citations
- Population genomics of domestic and wild yeasts (2008) — 1,478 citations
- Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning (2016) — 144 citations
- Mapping cis- and trans-regulatory effects across multiple tissues in twins (2012) — 807 citations
- Simultaneous assay of every Salmonella Typhi gene using one million transposon mutants. (2009) — 650 citations