{"id":29,"date":"2024-03-29T10:12:38","date_gmt":"2024-03-29T09:12:38","guid":{"rendered":"https:\/\/www.genai-school.universite-paris-saclay.fr\/?page_id=29"},"modified":"2024-07-31T15:45:20","modified_gmt":"2024-07-31T13:45:20","slug":"program","status":"publish","type":"page","link":"https:\/\/www.genai-school.universite-paris-saclay.fr\/index.php\/program\/","title":{"rendered":"Program"},"content":{"rendered":"\n
<\/span>\"L\u2019attribut
\n

Program<\/strong><\/p>\n<\/div><\/div>\n\n\n\n

<\/p>\n\n\n\n

Tentative program<\/strong><\/h4>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\n
Monday 21<\/strong>st<\/strong><\/sup> October<\/strong><\/summary>\n
09:45 \u2013 11:45<\/strong><\/strong><\/h6>\n\n\n\n

Alasdair Newson
Title:<\/strong> TBA

Abstract<\/strong>
TBA<\/p>\n\n\n\n

14:00 \u2013 15:00<\/strong><\/h5>\n\n\n\n

Eric Moulines
Title:<\/strong> Score based denoising diffusion: An introduction

Abstract<\/strong>
TBA<\/p>\n\n\n\n

15:30 \u2013 17:30<\/strong><\/h5>\n\n\n\n

TBA
Title:<\/strong> TBA

Abstract<\/strong>
TBA<\/p>\n<\/details>\n\n\n\n

Tuesday 22nd<\/strong><\/sup> October<\/strong><\/summary>\n
9:00 \u2013 10:30<\/strong><\/h5>\n\n\n\n

TBA
Title:<\/strong> TBA

Abstract<\/strong>
TBA<\/p>\n\n\n\n

11:00 \u2013 12: 30<\/strong><\/h5>\n\n\n\n

Yunhao Tang
Title:<\/strong> Reinforcement from human feedback: Some theory and practice

Abstract<\/strong>
TBA<\/p>\n\n\n\n

14:00 \u2013 16:00<\/strong><\/h5>\n\n\n\n

Yunhao Tang
Title:<\/strong> Reinforcement from human feedback: Some theory and practice

Abstract<\/strong>
TBA<\/p>\n\n\n\n

16:30 \u2013 18:00<\/strong><\/h5>\n\n\n\n

TBA
Title:<\/strong> TBA

Abstract<\/strong>
TBA<\/p>\n<\/details>\n\n\n\n

Wednesday 23<\/strong>rd<\/strong><\/sup> October<\/strong><\/summary>\n
9:00 \u2013 11:00<\/strong><\/h5>\n\n\n\n

Xavier Almeda-Pineda
Title:<\/strong> Probabilistic ways to fuse audio-visual data: within and beyond deep learning

Abstract<\/strong>
TBA<\/p>\n\n\n\n

14:00 \u2013 15:30<\/strong><\/h5>\n\n\n\n

Merouane Debbah
Title:<\/strong> Large Language Models: The Falcon case study

Abstract<\/strong>
In this talk, we will introduce the Falcon series which are causal decoder-only models trained on a diverse high-quality corpora predominantly assembled from web data. We will discuss in detail on the challenges and opportunities offered by Open Source Large Language Models as well the main applications in the field of Telecommunication.

Bio<\/strong>
M\u00e9rouane Debbah is a researcher, educator and technology entrepreneur. Over his career, he has founded several public and industrial research centers, start-ups and is now Professor at  Khalifa University of Science and Technology in Abu Dhabi and founding Director of the KU 6G Research Center. He is also the Chief Scientific AI Advisor at the Technology Innovation Institute. He is a frequent keynote speaker at international events in the field of telecommunication and AI. His research has been lying at the interface of fundamental mathematics, algorithms, statistics, information and communication sciences with a special focus on random matrix theory and learning algorithms. In the Communication field, he has been at the heart of the development of small cells (4G), Massive MIMO (5G) and Large Intelligent Surfaces (6G) technologies. In the AI field, he is known for his work on Large Language Models, distributed AI systems for networks and semantic communications. He received multiple prestigious distinctions, prizes and best paper awards (more than 40 IEEE best paper awards) for his contributions to both fields and according to research.com is ranked as the best scientist in France in the field of Electronics and Electrical Engineering. He is an IEEE Fellow, a WWRF Fellow, a Eurasip Fellow, an AAIA Fellow, an Institut Louis Bachelier Fellow and a Membre \u00e9m\u00e9rite SEE. His recent work led to the development of NOOR (upon it release, largest language model in Arabic) released in 2022, Falcon LLM (upon its release, top ranked open source large language model) released in 2023 and the Falcon Foundation in 2024. The Falcon Model Series and The Falcon Foundation have positioned the UAE as a global leader in the generative AI field. He is a member of the Marconi Prize Selection Advisory Committee.<\/p>\n\n\n\n

16:00 \u2013 17:30<\/strong><\/h5>\n\n\n\n

Claire Monteleoni
Title:<\/strong> AI Research for Climate Change and Environmental Sustainability.
<\/strong>

Abstract<\/strong>
TBA<\/p>\n<\/details>\n\n\n\n

Thursday<\/strong> 24th October<\/strong><\/summary>\n

<\/p>\n\n\n\n

9:00 \u2013 10:30<\/strong><\/h5>\n\n\n\n

Denis Trystram
Title:<\/strong> TBA

Abstract<\/strong>
TBA<\/p>\n\n\n\n

11:00 \u2013 12:30<\/strong><\/h5>\n\n\n\n

Claire Boyer
Title:<\/strong> A primer on physics-informed machine learning

Abstract<\/strong>
Physics-informed machine learning combines the expressiveness of data-based approaches with the interpretability of physical models. In this context, we consider a general regression problem where the empirical risk is regularized by a partial differential equation that quantifies the physical inconsistency. We prove that for linear differential priors, the problem can be formulated as a kernel regression task, giving a rigorous framework to analyze physics-informed ML. In particular, the physical prior can help in boosting the estimator convergence. The direct implementation of physics-informed kernel estimators can be tedious, and practitioners often resort to physics-informed neural networks (PINNs) instead. We offer some food for thought and statistical insight into the proper use of PINNs.<\/p>\n\n\n\n

14:00 \u2013 15:30<\/strong><\/h5>\n\n\n\n

Gael Richard
Title:<\/strong> Audio Generative AI

Abstract:<\/strong> TBA<\/p>\n\n\n\n

16:00 \u2013 17:30<\/strong><\/h5>\n\n\n\n

TBA
Title:<\/strong> TBA

Abstract<\/strong>
TBA<\/p>\n<\/details>\n\n\n\n

Friday 25<\/strong>th<\/strong><\/sup> October<\/strong><\/summary>\n

<\/p>\n\n\n\n

9:00 \u2013 10:30<\/strong><\/h5>\n\n\n\n

Antoine Bossulet
Title:<\/strong> TBA

Abstract<\/strong>
TBA<\/p>\n\n\n\n

11:00 \u2013 12:30<\/strong><\/h5>\n\n\n\n

Vicky Kalogeiton
Title:<\/strong> TBA

Abstract<\/strong>
TBA<\/p>\n\n\n\n

14:00 \u2013 15:30<\/strong><\/h5>\n\n\n\n

TBA
Title:<\/strong> TBA

Abstract<\/strong>
TBA<\/p>\n<\/details>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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