Fantine Mordelet
Molecular Biology · 2010–2012
A researcher in the Science corpus: 3 papers; trusts 30 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 35 citations into it; #5,876 of 17,773 by trust here, #13,563 by citations
- Artificial Intelligence — 20 citations into it; #18,608 of 35,345 by trust here, #31,361 by citations
- Statistics and Probability — 10 citations into it
- Statistical Learning Theory — 8 citations into it
- Computer Vision and Pattern Recognition — 7 citations into it
- Evaluation & Benchmarks — 6 citations into it
- Genetics — 6 citations into it; #1,963 of 6,854 by trust here, #5,952 by citations
Whom Fantine Mordelet trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Thorsten Joachims
- Leo Breiman
- David Ruppert
- Jean-Philippe Vert
- Victor A. McKusick
- Wee Sun Lee
- B. Liu
- Bernhard Schölkopf
- Trevor J. Hastie
- Laurent Jacob
Who trusts Fantine Mordelet overall
- Jessa Bekker
- Jesse Davis
- Nagarajan Natarajan
- Curtis G. Northcutt
- Vishrav Chaudhary
- Inderjit S. Dhillon
- Yu Zhang
- Tailin Wu
- Yuqing Tang
- Wenxu Wang
Papers from Fantine Mordelet's perspective
Ordered by how strongly the researchers Fantine Mordelet 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
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- A bagging SVM to learn from positive and unlabeled examples (2010) — 326 citations
- ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples (2011) — 183 citations
- TIGRESS: Trustful Inference of Gene REgulation using Stability Selection (2012) — 423 citations