Login

diiP Summer School 2024 - dSDS

Jun 10 – 14, 2024
Paris
Europe/Paris timezone

Contact:

Contribution List

19 out of 19 displayed
Export to PDF
  1. 1. AI for Research
    Themis Palpanas (LIPADE - Paris Descartes University), Yvonne Becherini (Université Paris Cité)
    6/10/24, 2:30 PM
  2. 2. Large Language Models (LLMs)
    Prof. Benoît Crabbé (Université Paris Cité)
    6/10/24, 4:30 PM

    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...

    Go to contribution page
  3. 3. Supervised Learning
    Prof. Alexandros Iosifidis (Aarhus University)
    6/11/24, 9:00 AM
  4. 4. Supervised Learning, Hands-on session
    Prof. Alexandros Iosifidis (Aarhus University)
    6/11/24, 11:00 AM
  5. 5. AI in Medicine/Biology
    Prof. Guillaume Assié (Institut Cochin, Inserm CNRS Université de Paris Cité)
    6/11/24, 12:00 PM
  6. 6. Representation Learning
    Dr Jenni Raitoharju (University of Jyväskylä)
    6/11/24, 2:30 PM
  7. 7. Representation Learning - Hands-on session
    Dr Jenni Raitoharju (University of Jyväskylä)
    6/11/24, 4:30 PM
  8. 8. Knowledge-guided Data Science
    Shen Liang
    6/12/24, 9:00 AM

    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.

    Go to contribution page
  9. 9. Knowledge-guided Data Science - Hands-on session (Basic)
    Shen Liang
    6/12/24, 11:00 AM
  10. 11. High-Dimensional Vector Similarity Search
    Themis Palpanas (LIPADE - Paris Descartes University)
    6/12/24, 2:30 PM

    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...

    Go to contribution page
  11. 12. An Overview of Anomaly Detection for Time Series
    Dr Paul Boniol (INRIA & ENS)
    6/13/24, 9:00 AM

    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...

    Go to contribution page
  12. 14. Time Series Anomaly Detection in Practice - Hands-on session
    Dr Paul Boniol
    6/13/24, 11:00 AM
  13. 13. AI in Industry
    Dr Paul Poncet (Engie)
    6/13/24, 12:00 PM
  14. 15. Graph Neural Networks
    Prof. Michalis Vazirgiannis
    6/13/24, 2:30 PM
  15. 16. Graph Generative AI + Applications
    Prof. Michalis Vazirgiannis (École polytechnique, Palaiseau)
    6/13/24, 4:30 PM
  16. 17. Generative adversarial networks and Active Learning
    Prof. Amal Saadallah (TU Dortmund University)
    6/14/24, 9:00 AM
  17. 18. Generative adversarial networks and Active Learning - Hands-on session
    Prof. Amal Saadallah (TU Dortmund University)
    6/14/24, 11:00 AM
  18. 19. AI in Particle Physics
    Dr Yann Coadou (CPPM, Aix-Marseille Université, CNRS/IN2P3)
    6/14/24, 12:00 PM

    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...

    Go to contribution page
  19. 10. Language Models