100 citations
- University of Milano-BicoccaIT8 papers
- National Research CouncilLK7 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di Milano BicoccaIT4 papers
- University of EdinburghGB3 papers
- Courant Institute of Mathematical SciencesUS2 papers
- New York UniversityUS2 papers
- Sapienza University of RomeIT2 papers
- Università degli Studi del Piemonte Orientale “Amedeo Avogadro”IT2 papers
- University of GöttingenDE2 papers
- University of MilanIT2 papers
- Acellera (Spain)ES1 paper
- Athinoula A. Martinos Center for Biomedical ImagingUS1 paper
16 papers
LUME-DBN: Full Bayesian Learning of DBNs from Incomplete data in Intensive Care
Federico Pirola, Fabio Stella, Marco Grzegorczyk
Dynamic Bayesian networks (DBNs) are increasingly used in healthcare due to their ability to model complex temporal relationships in patient data while maintaining interpretability…
mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics
Antonio Mirarchi, Toni Giorgino, Gianni De Fabritiis
Recent advancements in protein structure determination are revolutionizing our understanding of proteins. Still, a significant gap remains in the availability of comprehensive data…
Neural Networks unveiling the properties of gravitational wave background from massive black hole binaries
Matteo Bonetti, Alessia Franchini, Bruno Giovanni Galuzzi +1
Massive black hole binaries (MBHBs) are binary systems formed by black holes with mass exceeding millions of solar masses, expected to form and evolve in the nuclei of galaxies. Th…
A General Framework for Interpretable Neural Learning based on Local Information-Theoretic Goal Functions
Abdullah Makkeh, Marcel Graetz, Andreas C. Schneider +3
Despite the impressive performance of biological and artificial networks, an intuitive understanding of how their local learning dynamics contribute to network-level task solutions…
Quick Adaptive Ternary Segmentation: An Efficient Decoding Procedure For Hidden Markov Models
Alexandre Mösching, Housen Li, Axel Munk
Hidden Markov models (HMMs) are characterized by an unobservable Markov chain and an observable process -- a noisy version of the hidden chain. Decoding the original signal from th…
Multimodal and multicontrast image fusion via deep generative models
Giovanna Maria Dimitri, Simeon Spasov, Andrea Duggento +3
Recently, it has become progressively more evident that classic diagnostic labels are unable to reliably describe the complexity and variability of several clinical phenotypes. Thi…