Description
Date depot: 13 mars 2019
Titre: Deep Probabilistic Modeling
Directeur de thèse:
Maurizio FILIPPONE (Eurecom)
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Non defini
Resumé:
Today, we have access to so much data
generated by a variety of sensors, but we are facing difficulties in using
these data in a sensible way.
Machine Learning and Statistics offer
the main tools to help making sense of data, and novel techniques in this
domain will be used and developed throughout this project.
Quantification of risk and
decision-making require accurate quantification of uncertainty, which is a
major challenge in many areas of sciences involving complex phenomena like in
finance, environmental and life sciences.
In order to accurately quantify
uncertainty, we employ flexible and accurate tools offered by modern
statistical models. However, today's diversity and abundance of data make it
difficult to use these models in practice. The goal of this project is to
propose new ways to better manage the interface between computational and
statistical models - which in turn will help get accurate quantification of the
confidence in the predictions based on observed data.
The specific objectives of this project
are as follows:
Objective 1: Proposing and implementing
novel methodological advancements to scale and distribute computations for
modern probabilistic models, with emphasis on Bayesian Deep Models;
Objective 2: Explore the capabilities of
Bayesian Deep Models to accurately quantify uncertainty in the analysis of data
in life or environmental sciences.
In order to tackle the objectives above, the
Ph.D. project will make contributions in the areas of variational Inference for
Bayesian Deep Nets and Conv Nets, as well as the characterization of priors for
such deep models.
Doctorant.e: Rossi Simone