andreazugarini.github.io/index.xml at master · andreazugarini/andreazugarini.github.io · GitHub

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    <title>Andrea Zugarini</title>
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      <title>Andrea Zugarini</title>
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      <title>An Optimal Control Approach to Learning in SIDARTHE Epidemic model</title>
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      <pubDate>Wed, 11 Nov 2020 10:16:59 +0100</pubDate>
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      <title>Vulgaris: Analysis of a Corpus for Middle-Age Varieties of Italian Language</title>
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      <pubDate>Wed, 11 Nov 2020 10:16:47 +0100</pubDate>
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      <title>Vulgaris</title>
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      <pubDate>Sun, 11 Oct 2020 15:12:09 +0200</pubDate>
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      <description>&lt;p&gt;The main goal of the project is the analysis of the diachronic evolution and variance of the vulgar Italian language.
In order to do so, we retrieved an heterogeneous literary text corpus from Biblioteca Italiana, a digital library project collecting the most significant texts of the Italian literature, ranging from the Middle Age to the 20th century.
The analyzed corpus contains poetry, prose, epistles and correspondence by the most important Italian authors ranging from the dawn of the vulgar language to the Reinassance Age.&lt;/p&gt;
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      <title>Neural Poetry</title>
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      <pubDate>Mon, 21 Sep 2020 22:22:18 +0200</pubDate>
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      <description>&lt;p&gt;We study the problem of neural poem generation, focussing on Dante Alighieri, widely known for his Divine Comedy. This challenging task, where the machine has to capture the linguistic features that characterize the poet from the few available author&amp;rsquo;s works, it is addressed by using a syllable-based neural language model trained with a multi-stage procedure that exploits non-poetic works of the same author and also other publicly available huge modern Italian corpora.&lt;/p&gt;
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      <title>Learning Epidemiological Models</title>
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      <pubDate>Mon, 21 Sep 2020 22:21:25 +0200</pubDate>
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      <description>&lt;p&gt;The COVID-19 outbreak has stimulated the interest in the proposal of novel epidemiological models to predict the course of the epidemic so as to help planning effective control strategies. In particular, in order to properly interpret the available data, it has become clear that one must go beyond most classic epidemiological models and consider models that, like the recently proposed SIDARTHE, offer a richer description of the stages of infection. The problem of learning the parameters of these models is of crucial importance especially when assuming that they are time-variant, which further enriches their effectiveness. This project focuses on developing a general approach for learning time-variant parameters of dynamic compartmental models from epidemic data.&lt;/p&gt;
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      <title>An Unsupervised Character-Aware Neural Approach to Word and Context Representation Learning</title>
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      <title>Combining deep learning and symbolic processing for extracting knowledge from raw text</title>
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      <title>Learning in Text Streams: Discovery and Disambiguation of Entity and Relation Instances</title>
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      <pubDate>Tue, 01 Jan 2019 00:00:00 +0000</pubDate>
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      <description></description>
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      <title>Neural Paraphrasing by Automatically Crawled and Aligned Sentence Pairs</title>
      <link>/publication/globo-2019-neural/</link>
      <pubDate>Tue, 01 Jan 2019 00:00:00 +0000</pubDate>
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      <description></description>
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      <title>Neural Poetry: Learning to Generate Poems Using Syllables</title>
      <link>/publication/zugarini-2019-neural/</link>
      <pubDate>Tue, 01 Jan 2019 00:00:00 +0000</pubDate>
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      <description></description>
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      <title>A new integrated and interactive tool applicable to inborn errors of metabolism: Application to alkaptonuria</title>
      <link>/publication/spiga-2018-new/</link>
      <pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate>
      <guid>/publication/spiga-2018-new/</guid>
      <description></description>
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      <title>Neural networks for beginners. A fast implementation in matlab, torch, tensorflow</title>
      <link>/publication/giannini-2017-neural/</link>
      <pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate>
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