prof. Pietro Michiardi, Ph.D. presents Generative Diffusion Models: Key Ideas, Current Practice and Research Challenges
On 2023-04-28 11:00:00 at Technická 2, Praha 6, room T2:D3-309 and online http://bit.ly/40IAORS
prof. Pietro Michiardi, Ph.D., EURECOM, Sophia Antipolis, France
Generative Diffusion Models: Key Ideas, Current Practice and Research
Challenges
Abstract: In this talk, we will discuss a special class of generative models,
called diffusion processes, and present their applications as well as
challenges
that the scientific community is currently addressing.
We will first provide some background for the generative modelling task, and
introduce the basic tools that are required to understand diffusion processes.
We will then quickly overview the general topic of energy-based modelling, give
some example applications, and focus on score-based generative models. Our goal
is to provide an overview of the theory behind score-based models, but also to
demystify them and suggest a practical interpretation of these methods.
We will conclude the talk with some challenges we are currently addressing,
including going beyond image data, multi-modality, and methodological advances
that are required to improve the practical application of such exciting new
methods.
Bio: Pietro Michiardi received his M.S. in Electrical Engineering from
Politecnico di Torino and his M.S. in Computer Science from EURECOM. Pietro
received his Ph.D. in Computer Science from Telecom ParisTech, and his HDR
(Habilitation) from UCA.
Today, Pietro is a Full Professor and head of the Data Science Department at
EURECOM. In his work, Pietro focuses on scalable machine learning algorithms,
Bayesian modelling and sampling, generative modelling and representation
learning. Pietro is interested in developing a theoretical understanding of
methods that learn from data, in methodological aspects of computationally
efficient inference approaches, and their application to industrial domains
such
as the automotive, telecommunication and financial industries.
In the past, Pietro worked on a wide range of research topics, including:
computer networks and their performance evaluation, applied cryptography,
applied game theory, distributed systems, and data management systems.
http://www.eurecom.fr/~michiard
https://scholar.google.com/citations?user=mlx1eCgAAAAJ&hl=en&oi=ao
Join the talk in MS Teams:
https://teams.microsoft.com/l/meetup-join/19%3ameeting_M2MxOTU1MTktYjU2Ny00MzJmLTgwZGMtNzc1MzM2OTI5MWU3%40thread.v2/0?context=%7b%22Tid%22%3a%22f345c406-5268-43b0-b19f-5862fa6833f8%22%2c%22Oid%22%3a%22255e49af-0da3-4d29-bf80-5c8bc8d00a4d%22%7d
Generative Diffusion Models: Key Ideas, Current Practice and Research
Challenges
Abstract: In this talk, we will discuss a special class of generative models,
called diffusion processes, and present their applications as well as
challenges
that the scientific community is currently addressing.
We will first provide some background for the generative modelling task, and
introduce the basic tools that are required to understand diffusion processes.
We will then quickly overview the general topic of energy-based modelling, give
some example applications, and focus on score-based generative models. Our goal
is to provide an overview of the theory behind score-based models, but also to
demystify them and suggest a practical interpretation of these methods.
We will conclude the talk with some challenges we are currently addressing,
including going beyond image data, multi-modality, and methodological advances
that are required to improve the practical application of such exciting new
methods.
Bio: Pietro Michiardi received his M.S. in Electrical Engineering from
Politecnico di Torino and his M.S. in Computer Science from EURECOM. Pietro
received his Ph.D. in Computer Science from Telecom ParisTech, and his HDR
(Habilitation) from UCA.
Today, Pietro is a Full Professor and head of the Data Science Department at
EURECOM. In his work, Pietro focuses on scalable machine learning algorithms,
Bayesian modelling and sampling, generative modelling and representation
learning. Pietro is interested in developing a theoretical understanding of
methods that learn from data, in methodological aspects of computationally
efficient inference approaches, and their application to industrial domains
such
as the automotive, telecommunication and financial industries.
In the past, Pietro worked on a wide range of research topics, including:
computer networks and their performance evaluation, applied cryptography,
applied game theory, distributed systems, and data management systems.
http://www.eurecom.fr/~michiard
https://scholar.google.com/citations?user=mlx1eCgAAAAJ&hl=en&oi=ao
Join the talk in MS Teams:
https://teams.microsoft.com/l/meetup-join/19%3ameeting_M2MxOTU1MTktYjU2Ny00MzJmLTgwZGMtNzc1MzM2OTI5MWU3%40thread.v2/0?context=%7b%22Tid%22%3a%22f345c406-5268-43b0-b19f-5862fa6833f8%22%2c%22Oid%22%3a%22255e49af-0da3-4d29-bf80-5c8bc8d00a4d%22%7d