Search Results for author: Giuseppe Jurman

Found 11 papers, 1 papers with code

MASS-UMAP: Fast and accurate analog ensemble search in weather radar archive

no code implementations1 Oct 2019 Gabriele Franch, Giuseppe Jurman, Luca Coviello, Marta Pendesini, Cesare Furlanello

The most challenging aspect of using this approach in the context of operational radar applications is to be able to perform a fast and accurate search for similar spatiotemporal precipitation patterns in a large archive of historical records.

Dimensionality Reduction Time Series +1

Not again! Data Leakage in Digital Pathology

no code implementations14 Sep 2019 Nicole Bussola, Alessia Marcolini, Valerio Maggio, Giuseppe Jurman, Cesare Furlanello

We verify that accuracy scores may be inflated up to 41%, even if a well-designed 10x5 iterated cross-validation DAP is applied, unless all images from the same subject are kept together either in the internal training or validation splits.

Model Selection Transfer Learning

High Resolution Forecasting of Heat Waves impacts on Leaf Area Index by Multiscale Multitemporal Deep Learning

no code implementations13 Sep 2019 Andrea Gobbi, Marco Cristoforetti, Giuseppe Jurman, Cesare Furlanello

The historical weather data are then replaced with forecast values to predict LAI values at 10 day horizon on Europe.

A multiobjective deep learning approach for predictive classification in Neuroblastoma

no code implementations22 Nov 2017 Valerio Maggio, Marco Chierici, Giuseppe Jurman, Cesare Furlanello

Neuroblastoma is a strongly heterogeneous cancer with very diverse clinical courses that may vary from spontaneous regression to fatal progression; an accurate patient's risk estimation at diagnosis is essential to design appropriate tumor treatment strategies.

Classification General Classification

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

no code implementations16 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

Deep Learning for Automatic Stereotypical Motor Movement Detection using Wearable Sensors in Autism Spectrum Disorders

no code implementations14 Sep 2017 Nastaran Mohammadian Rad, Seyed Mostafa Kia, Calogero Zarbo, Twan van Laarhoven, Giuseppe Jurman, Paola Venuti, Elena Marchiori, Cesare Furlanello

Our results show that: 1) feature learning outperforms handcrafted features; 2) parameter transfer learning is beneficial in longitudinal settings; 3) using LSTM to learn the temporal dynamic of signals enhances the detection rate especially for skewed training data; 4) an ensemble of LSTMs provides more accurate and stable detectors.

Ensemble Learning Transfer Learning

Phylogenetic Convolutional Neural Networks in Metagenomics

no code implementations6 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.

Classification Domain Adaptation +1

Towards meaningful physics from generative models

no code implementations26 May 2017 Marco Cristoforetti, Giuseppe Jurman, Andrea I. Nardelli, Cesare Furlanello

In several physical systems, important properties characterizing the system itself are theoretically related with specific degrees of freedom.

Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism

no code implementations5 Nov 2015 Nastaran Mohammadian Rad, Andrea Bizzego, Seyed Mostafa Kia, Giuseppe Jurman, Paola Venuti, Cesare Furlanello

Autism Spectrum Disorders (ASDs) are often associated with specific atypical postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have a specific visibility.

Transfer Learning

Sparse Predictive Structure of Deconvolved Functional Brain Networks

no code implementations24 Oct 2013 Tommaso Furlanello, Marco Cristoforetti, Cesare Furlanello, Giuseppe Jurman

The functional and structural representation of the brain as a complex network is marked by the fact that the comparison of noisy and intrinsically correlated high-dimensional structures between experimental conditions or groups shuns typical mass univariate methods.

Classification General Classification

mlpy: Machine Learning Python

1 code implementation29 Feb 2012 Davide Albanese, Roberto Visintainer, Stefano Merler, Samantha Riccadonna, Giuseppe Jurman, Cesare Furlanello

mlpy is a Python Open Source Machine Learning library built on top of NumPy/SciPy and the GNU Scientific Libraries.

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