AI for Research

• Themis Palpanas (LIPADE - Paris Descartes University)
• Yvonne Becherini (Université Paris Cité)

Room: 418C, Halle aux Farines

Large Language Models (LLMs)

• Benoît Crabbé (Université Paris Cité)

Room: 418C, Halle aux Farines Large language models have become ubiquitous. This lecture introduces to language modelling and large language models. We will discover what they are, where they come from and the primary motivations behind their design. We then provide an overview of the properties of these models when trained at the current scale of very large language models. If time remains, we introduce the problematic of explaining their behaviour.

Supervised Learning

• Alexandros Iosifidis (Aarhus University)

Room: 418C, Halle aux Farines

Supervised Learning, Hands-on session

• Alexandros Iosifidis (Aarhus University)

Room: 418C, Halle aux Farines

AI in Medicine/Biology

• Guillaume Assié (Institut Cochin, Inserm CNRS Université de Paris Cité)

Room: 418C, Halle aux Farines

Representation Learning

• Jenni Raitoharju (University of Jyväskylä)

Room: 418C, Halle aux Farines

Representation Learning - Hands-on session

• Jenni Raitoharju (University of Jyväskylä)

Room: 418C, Halle aux Farines

Knowledge-guided Data Science

• Shen Liang

Room: 418C, Halle aux Farines This lecture presents an overview of knowledge-guided data science, a rising methodology in machine learning which fuses data with domain knowledge. We will present numerous case studies on this methodology to showcase how to unleash its potential in real-world data science applications.

Knowledge-guided Data Science - Hands-on session (Basic)

• Shen Liang

Room: 418C, Halle aux Farines

High-Dimensional Vector Similarity Search

• Themis Palpanas (LIPADE - Paris Descartes University)

Room: 418C, Halle aux Farines Very large amounts of high-dimensional data are now omnipresent (ranging from traditional multidimensional data to time series and deep embeddings), and the performance requirements (i.e., response-time and accuracy) of a variety of applications that need to process and analyze these data have become very stringent and demanding. In the past years, high-dimensional similarity search has been studied in its many flavors. Similarity search algorithms for exact and approximate, one-off and progressive query answering. Approximate algorithms with and without (deterministic or probabilistic) quality guarantees. Solutions for on-disk and in-memory data, static and streaming data. Approaches based on multidimensional space-partitioning and metric trees, random projections and locality-sensitive hashing (LSH), product quantization (PQ) and inverted files, k-nearest neighbor graphs and optimized linear scans. Surprisingly, the work on data-series (or time-series) similarity search has recently been shown to achieve the state-of-the-art performance for several variations of the problem, on both time-series and general high-dimensional vector data. In this talk, we will touch upon the different aspects of this interesting story, and present some of the state-of-the-art solutions.

An Overview of Anomaly Detection for Time Series

• Paul Boniol (INRIA & ENS)

Room: 418C, Halle aux Farines Anomaly detection is an important problem in data analytics with applications in many domains. In recent years, there has been an increasing interest in anomaly detection tasks applied to time series. In this talk, we take a holistic view of anomaly detection in time series, starting from the core definitions and taxonomies related to time series and anomaly types, to an extensive description of the anomaly detection methods proposed by different communities in the literature. We will then present new benchmarks capturing diverse domains and applications for the purpose of evaluating anomaly detection methods. We will then conclude on Ensembling and Model Selection for time series anomaly detection, discussing different strategies applicable to automatically selecting the appropriate methods for a specific time series.

Time Series Anomaly Detection in Practice - Hands-on session

• Paul Boniol

Room: 418C, Halle aux Farines

AI in Industry

• Paul Poncet (Engie)

Room: 418C, Halle aux Farines

Graph Neural Networks

• Michalis Vazirgiannis

Room: 418C, Halle aux Farines

Graph Generative AI + Applications

• Michalis Vazirgiannis (École polytechnique, Palaiseau)

Room: 418C, Halle aux Farines

Generative adversarial networks and Active Learning

• Amal Saadallah (TU Dortmund University)

Room: 418C, Halle aux Farines

Generative adversarial networks and Active Learning - Hands-on session

• Amal Saadallah (TU Dortmund University)

Room: 418C, Halle aux Farines

AI in Particle Physics

• Yann Coadou (CPPM, Aix-Marseille Université, CNRS/IN2P3)

Room: 418C, Halle aux Farines Particle physics deals with gigantic machines, large quantities of experimental data and computer simulations, complex and lengthy theoretical calculations. It is the perfect playground to take advantage of machine learning algorithms. After a short introduction to high energy physics, this lecture will show how one can speed up steps like event generation or detector simulation, better measure parameters, improve classification of events as signal or background, or discover anomalies in data taking, through various applications of machine learning in this field.