Published February 11, 2022
| Version v1
Publication
Principled methods for mixtures processing
Creators
Contributors
Others:
- Scientific Data Management (ZENITH) ; Centre Inria d'Université Côte d'Azur (CRISAM) ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM) ; Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)
- Université de Montpellier
- Christian Jutten(christian.jutten@gipsa-lab.grenoble-inp.fr)
Description
This document is my thesis for getting the habilitation à diriger des recherches, which is the french diploma that is required to fully supervise Ph.D. students. It summarizes the research I did in the last 15 years and also provides the shortterm research directions and applications I want to investigate. Regarding my past research, I first describe the work I did on probabilistic audio modeling, including the separation of Gaussian and αstable stochastic processes. Then, I mention my work on deep learning applied to audio, which rapidly turned into a large effort for community service. Finally, I present my contributions in machine learning, with some works on hardware compressed sensing and probabilistic generative models.My research programme involves a theoretical part that revolves around probabilistic machine learning, and an applied part that concerns the processing of time series arising in both audio and life sciences.
Additional details
Identifiers
- URL
- https://inria.hal.science/tel-03578077
- URN
- urn:oai:HAL:tel-03578077v1
Origin repository
- Origin repository
- UNICA