21 citations · 58 across the 7 of their papers we have counts for
7 papers
False membership rate control in mixture models
Ariane Marandon, Tabea Rebafka, Etienne Roquain +1
The clustering task consists in partitioning elements of a sample into homogeneous groups. Most datasets contain individuals that are ambiguous and intrinsically difficult to attri…
Supervised deep learning prediction of the formation enthalpy of the full set of configurations in complex phases: the phase as an example
Jean-Claude Crivello, Nataliya Sokolovska, Jean-Marc Joubert
Machine learning (ML) methods are becoming integral to scientific inquiry in numerous disciplines, such as material sciences. In this manuscript, we demonstrate how ML can be used…
Latent Instrumental Variables as Priors in Causal Inference based on Independence of Cause and Mechanism
Nataliya Sokolovska, Pierre-Henri Wuillemin
Causal inference methods based on conditional independence construct Markov equivalent graphs, and cannot be applied to bivariate cases. The approaches based on independence of cau…
CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks
Asma Nouira, Nataliya Sokolovska, Jean-Claude Crivello
Our main motivation is to propose an efficient approach to generate novel multi-element stable chemical compounds that can be used in real world applications. This task can be form…
Disease Classification in Metagenomics with 2D Embeddings and Deep Learning
Thanh Hai Nguyen, Edi Prifti, Yann Chevaleyre +2
Deep learning (DL) techniques have shown unprecedented success when applied to images, waveforms, and text. Generally, when the sample size () is much bigger than the number of…
Deep Learning for Metagenomic Data: using 2D Embeddings and Convolutional Neural Networks
Thanh Hai Nguyen, Yann Chevaleyre, Edi Prifti +2
Deep learning (DL) techniques have had unprecedented success when applied to images, waveforms, and texts to cite a few. In general, when the sample size (N) is much greater than t…
Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labelling
Nataliya Sokolovska, Thomas Lavergne, Olivier Cappé +1
Conditional Random Fields (CRFs) constitute a popular and efficient approach for supervised sequence labelling. CRFs can cope with large description spaces and can integrate some f…