Robust Estimation under Linear Mixed Models: The Minimum Density Power Divergence Approach

no code implementations • 12 Oct 2020 • Giovanni Saraceno, Abhik Ghosh, Ayanendranath Basu, Claudio Agostinelli

Many real-life data sets can be analyzed using Linear Mixed Models (LMMs).

Methodology Applications 62F35, 62J05

Convolutional neural networks for structured omics: OmicsCNN and the OmicsConv layer

no code implementations • 16 Oct 2017 • Giuseppe Jurman, Valerio Maggio, Diego Fioravanti, Ylenia Giarratano, Isotta Landi, Margherita Francescatto, Claudio Agostinelli, Marco Chierici, Manlio De Domenico, Cesare Furlanello

Convolutional Neural Networks (CNNs) are a popular deep learning architecture widely applied in different domains, in particular in classifying over images, for which the concept of convolution with a filter comes naturally.

Semantic Similarity Semantic Textual Similarity

Phylogenetic Convolutional Neural Networks in Metagenomics

no code implementations • 6 Sep 2017 • Diego Fioravanti, Ylenia Giarratano, Valerio Maggio, Claudio Agostinelli, Marco Chierici, Giuseppe Jurman, Cesare Furlanello

We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure.

Domain Adaptation General Classification