Davide D'Ascenzo

Ph.D. candidate in Intelligenza Artificiale , 38^th cycle (2022-2025)
Department of Control and Computer Engineering (DAUIN)

Theory and Tools for Structure-Aware Machine Learning on Biological Data

Cultrera Di Montesano, Sebastiano; D'Ascenzo, Davide; Raghavan, Srivatsan; Amini, Ava ... (In stampa)
Hierarchical cross-entropy loss improves atlas-scale single-cell annotation models. In: NATURE COMPUTATIONAL SCIENCE. ISSN 2662-8457
Contributo su Rivista
D'Ascenzo, Davide; Cultrera Di Montesano, Sebastiano (2025)
scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omics.
Altro
Danhofer, David A.; D'Ascenzo, Davide; Dubach, Rafael; Poggio, Tomaso (2025)
Position: A Theory of Deep Learning Must Include Compositional Sparsity. In: Forty-second International Conference on Machine Learning Position, Vancouver (CAN), July 13th -19th, 2025, pp. 81199-81210
Contributo in Atti di Convegno (Proceeding)
Boldi, Paolo; D'Ascenzo, Davide; Furia, Flavio; Vigna, Sebastiano (2024)
Score and rank semi-monotonicity for closeness, betweenness, and distance–decay centralities. In: SOCIAL NETWORK ANALYSIS AND MINING, vol. 14. ISSN 1869-5469
Contributo su Rivista
Boldi, Paolo; D'Ascenzo, Davide; Furia, Flavio; Vigna, Sebastiano (2024)
Score and Rank Semi-Monotonicity for Closeness, Betweenness and Harmonic Centrality. In: The Twelfth International Conference on Complex Networks and their Applications, Menton (FRA), 28 - 30 November, 2023, pp. 102-113. ISSN 1860-949X. ISBN: 9783031534713
Contributo in Atti di Convegno (Proceeding)