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Achieving Predictive Precision: Leveraging LSTM and Pseudo Labeling for Volvo's Discovery Challenge at ECML-PKDD 2024

Autor: Metta, Carlo, Gregnanin, Marco, Papini, Andrea, Galfrè, Silvia Giulia, Fois, Andrea, Morandin, Francesco, Fantozzi, Marco, Parton, Maurizio

This paper presents the second-place methodology in the Volvo Discovery Challenge at ECML-PKDD 2024, where we used Long Short-Term Memory networks and pseudo-labeling to predict maintenance needs for a component of Volvo trucks. We processed the trai

Externí odkaz: http://arxiv.org/abs/2409.13877

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A Systematization of the Wagner Framework: Graph Theory Conjectures and Reinforcement Learning

Autor: Angileri, Flora, Lombardi, Giulia, Fois, Andrea, Faraone, Renato, Metta, Carlo, Salvi, Michele, Bianchi, Luigi Amedeo, Fantozzi, Marco, Galfrè, Silvia Giulia, Pavesi, Daniele, Parton, Maurizio, Morandin, Francesco

In 2021, Adam Zsolt Wagner proposed an approach to disprove conjectures in graph theory using Reinforcement Learning (RL). Wagner's idea can be framed as follows: consider a conjecture, such as a certain quantity f(G) < 0 for every graph G; one can t

Externí odkaz: http://arxiv.org/abs/2406.12667

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Increasing biases can be more efficient than increasing weights

Autor: Metta, Carlo, Fantozzi, Marco, Papini, Andrea, Amato, Gianluca, Bergamaschi, Matteo, Galfrè, Silvia Giulia, Marchetti, Alessandro, Vegliò, Michelangelo, Parton, Maurizio, Morandin, Francesco

We introduce a novel computational unit for neural networks that features multiple biases, challenging the traditional perceptron structure. This unit emphasizes the importance of preserving uncorrupted information as it is passed from one unit to th

Externí odkaz: http://arxiv.org/abs/2301.00924

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A mathematical framework for raw counts of single-cell RNA-seq data analysis

Autor: Galfre', Silvia Giulia, Morandin, Francesco

Single-cell RNA-seq data are challenging because of the sparseness of the read counts, the tiny expression of many relevant genes, and the variability in the efficiency of RNA extraction for different cells. We consider a simple probabilistic model f

Externí odkaz: http://arxiv.org/abs/2002.02933

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