R. Greiner
Molecular Biology · Nucleic Acids Res. · 2001–2021
A researcher in the Science corpus: 19 papers; trusts 95 researchers and is trusted by 29 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 95 citations into it; #15,575 of 17,773 by trust here, #2,739 by citations
- Artificial Intelligence — 67 citations into it; #8,048 of 35,345 by trust here, #13,163 by citations
- Computational Theory and Mathematics — 37 citations into it; #8,959 of 12,182 by trust here, #2,060 by citations
- Representation & Self-Supervised Learning — 35 citations into it
- Spectroscopy — 30 citations into it
- Computer Vision and Pattern Recognition — 20 citations into it; #18,623 of 21,575 by trust here, #10,677 by citations
- Probabilistic & Bayesian Methods — 16 citations into it
- Statistical Learning Theory — 15 citations into it
Whom R. Greiner trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Shai Ben-David
- Yishay Mansour
- John Blitzer
- Mehryar Mohri
- J. Ross Quinlan
- Geoffrey E. Hinton
- Tom L. Burr
- Ron Kohavi
- Masashi Sugiyama
- Corinna Cortes
Who trusts R. Greiner overall
- S. Kotsiantis
- Ioannis Tsamardinos
- Constantin F. Aliferis
- Ziyun Wang
- Bobak Shahriari
- Luis M. de Campos
- Finn Verner Jensen
- Efstathios Kirkos
- Andreas Holzinger
- Chao Yu
Papers from R. Greiner's perspective
Ordered by how strongly the researchers R. Greiner 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
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- GENERATIVE ADVERSARIAL NETS
Their papers
- An experimental methodology for response surface optimization methods (2011) — 59 citations
- Active Model Selection (2004) — 79 citations
- Visual Explanation of Evidence with Additive Classifiers (2006) — 135 citations
- Learning Bayesian networks from data: An information-theory based approach (2002) — 833 citations
- HMDB: the Human Metabolome Database (2007) — 3,058 citations
- HMDB: a knowledgebase for the human metabolome (2008) — 1,846 citations
- HMDB 3.0—The Human Metabolome Database in 2013 (2012) — 2,793 citations
- Predicting subcellular localization of proteins using machine-learned classifiers (2004) — 359 citations
- CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra (2014) — 416 citations
- ClassyFire: automated chemical classification with a comprehensive, computable taxonomy (2016) — 1,336 citations
- Learning Bayesian Belief Network Classifiers: Algorithms and System (2001) — 332 citations
- Proteome Analyst: custom predictions with explanations in a web-based tool for high-throughput proteome annotations (2004) — 113 citations
- BASys: a web server for automated bacterial genome annotation (2005) — 324 citations
- BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification (2019) — 425 citations
- HMDB 5.0: the Human Metabolome Database for 2022 (2021) — 638 citations
- Domain Aggregation Networks for Multi-Source Domain Adaptation (2019) — 85 citations
- Robust Learning under Uncertain Test Distributions: Relating Covariate Shift to Model Misspecification (2014) — 118 citations
- Budgeted Learning for Developing Personalized Treatment (2014) — 7 citations
- A Fast Way to Produce Optimal Fixed-Depth Decision Trees (2008) — 9 citations