Luca Della Libera

Doctorat - Concordia

Superviseur⋅e principal⋅e

Mirco Ravanelli

Co-supervisor

Cem Subakan

Sujets de recherche

Apprentissage profond

GitHub

Billets de blogue

Visual of FocalCodec,nouvelle méthode pour compresser la parole sans sacrifier la qualité, en vue d'obtenir des LLM multimodaux plus efficaces.

23 janvier 2026

FocalCodec : donner l’ouïe et la parole aux LLM à débit ultra-faible

par

Luca Della Libera

Francesco Paissan

Cem Subakan

Mirco Ravanelli

Lire l'article

Publications

Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent

Luca Della Libera

Jacopo Andreoli

Davide Dalle Pezze

Mirco Ravanelli

Gian Antonio Susto

A crucial task in predictive maintenance is estimating the remaining useful life of physical systems. In the last decade, deep learning has … (voir plus)improved considerably upon traditional model-based and statistical approaches in terms of predictive performance. However, in order to optimally plan maintenance operations, it is also important to quantify the uncertainty inherent to the predictions. This issue can be addressed by turning standard frequentist neural networks into Bayesian neural networks, which are naturally capable of providing confidence intervals around the estimates. Several methods exist for training those models. Researchers have focused mostly on parametric variational inference and sampling-based techniques, which notoriously suffer from limited approximation power and large computational burden, respectively. In this work, we use Stein variational gradient descent, a recently proposed algorithm for approximating intractable distributions that overcomes the drawbacks of the aforementioned techniques. In particular, we show through experimental studies on simulated run-to-failure turbofan engine degradation data that Bayesian deep learning models trained via Stein variational gradient descent consistently outperform with respect to convergence speed and predictive performance both the same models trained via parametric variational inference and their frequentist counterparts trained via backpropagation. Furthermore, we propose a method to enhance performance based on the uncertainty information provided by the Bayesian models. We release the source code at https://github.com/lucadellalib/bdl-rul-svgd.

2024-02-02

ArXiv (prépublication)

doi.org

arxiv.org

CL-MASR: A Continual Learning Benchmark for Multilingual ASR

2024-01-01

IEEE ACM Trans. Audio Speech Lang. Process. (publié)