no code implementations • 26 May 2024 • Gabriele Dominici, Pietro Barbiero, Mateo Espinosa Zarlenga, Alberto Termine, Martin Gjoreski, Giuseppe Marra, Marc Langheinrich
Causal opacity denotes the difficulty in understanding the "hidden" causal structure underlying a deep neural network's (DNN) reasoning.
no code implementations • 26 May 2024 • Gabriele Dominici, Pietro Barbiero, Francesco Giannini, Martin Gjoreski, Marc Langhenirich
Interpretable deep learning aims at developing neural architectures whose decision-making processes could be understood by their users.
no code implementations • 24 May 2024 • Dario Fenoglio, Gabriele Dominici, Pietro Barbiero, Alberto Tonda, Martin Gjoreski, Marc Langheinrich
Federated Learning (FL), a privacy-aware approach in distributed deep learning environments, enables many clients to collaboratively train a model without sharing sensitive data, thereby reducing privacy risks.
1 code implementation • 2 Feb 2024 • Gabriele Dominici, Pietro Barbiero, Francesco Giannini, Martin Gjoreski, Giuseppe Marra, Marc Langheinrich
"), and imagine alternative scenarios that could result in different predictions (the "What if?").
1 code implementation • 1 Jul 2023 • Gabriele Dominici, Pietro Barbiero, Lucie Charlotte Magister, Pietro Liò, Nikola Simidjievski
Multimodal learning is an essential paradigm for addressing complex real-world problems, where individual data modalities are typically insufficient to accurately solve a given modelling task.