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2020 – today
- 2024
[i3]Marco D'Alessandro, Enrique Calabrés
, Mikel Elkano:
A Modular End-to-End Multimodal Learning Method for Structured and Unstructured Data. CoRR abs/2403.04866 (2024)- 2023
- [j3]Gian Luca Lancia, Mattia Eluchans, Marco D'Alessandro, Hugo J. Spiers, Giovanni Pezzulo:
Humans account for cognitive costs when finding shortcuts: An information-theoretic analysis of navigation. PLoS Comput. Biol. 19(1) (2023) - [j2]Stefan T. Radev, Marco D'Alessandro, Ulf K. Mertens, Andreas Voss, Ullrich Köthe, Paul-Christian Bürkner:
Amortized Bayesian Model Comparison With Evidential Deep Learning. IEEE Trans. Neural Networks Learn. Syst. 34(8): 4903-4917 (2023) - [c1]Marco D'Alessandro, Alberto Alonso, Enrique Calabrés, Mikel Galar:
Multimodal Parameter-Efficient Few-Shot Class Incremental Learning. ICCV (Workshops) 2023: 3385-3395 - [i2]Marco D'Alessandro, Alberto Alonso, Enrique Calabrés, Mikel Galar:
Multimodal Parameter-Efficient Few-Shot Class Incremental Learning. CoRR abs/2303.04751 (2023) - 2021
- [j1]Antonino Greco, Giuseppe Gallitto, Marco D'Alessandro, Clara Rastelli:
Increased Entropic Brain Dynamics during DeepDream-Induced Altered Perceptual Phenomenology. Entropy 23(7): 839 (2021) - 2020
- [i1]Stefan T. Radev, Marco D'Alessandro, Paul-Christian Bürkner, Ulf K. Mertens, Andreas Voss, Ullrich Köthe:
Amortized Bayesian model comparison with evidential deep learning. CoRR abs/2004.10629 (2020)
Coauthor Index
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