Mattia Desana

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Sum-Product Networks with complex probability distribution at the leaves have been shown to be powerful tractable-inference probabilistic models. However, while learning the internal parameters has been amply studied, learning complex leaf distribution is an open problem with only few results available in special cases. In this paper we derive an efficient(More)
The behaviour of glycaemia, insulinaemia, phosphoraemia, somatotropinaemia,free glycerol and triglyceridaemia was studied in six patients with A.L.S. following sugar load (1 g/Kg) in fasting. The results of glycaemia and insulinaemia were in tune with published data which have pointed to reduced sugar tolerance and reduced insulin secretion in patients with(More)
Segmenting retinal tissue deformed by pathologies can be challenging. Segmentation approaches are often constructed with a certain pathology in mind and may require a large set of labeled pathological scans, and therefore are tailored to that particular pathology. We present an approach that can be easily transfered to new pathologies, as it is designed(More)
This paper introduces a new probabilistic architecture called Sum-Product Graphical Model (SPGM). SPGMs combine traits from Sum-Product Networks (SPNs) and Graphical Models (GMs): Like SPNs, SPGMs always enable tractable inference using a class of models that incorporate context specific independence. Like GMs, SPGMs provide a high-level model(More)
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