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Titre : Improving Scalability and Inference in Probabilistic Deep Models
Date : 21 février 2022
Heure: 14:00
Résumé : Throughout the last decade, deep learning has reached a sufficient level of maturity to become the preferred choice to solve machine learning-related problems or to aid decision making processes. At the same time, deep learning is generally not equipped with the ability to accurately quantify the uncertainty of its predictions, thus making these models less suitable for risk-critical applications. A possible solution to address this problem is to employ a Bayesian formulation; however, while this offers an elegant treatment, it is analytically intractable and it requires approximations. Despite the huge advancements in the last few years, there is still a long way to make these approaches widely applicable. In this thesis, we address some of the challenges for modern Bayesian deep learning, by proposing and studying solutions to improve scalability and inference of these models.
Lieu : EURECOM