Clinique des données
17 mars 2025 @ 14 h 00 min – CBGP, Grande salle de réunion du CBGP (Présentiel) La Clinique des données est un service mis en place par l’ISDM afin de porter assistance à la communauté scientifique sur des thématique liées aux données. Ainsi, toute personne ayant une problématique, une question, un bug est la bienvenue lors des permanences de ce service. Vous serez […]
Explainable and Interpretable Learning: Making Sense on Complex Modeling Domains
26 mars 2025 @ 14 h 00 min –
Room Nadir, Maison de la Télédétection, Agropolis campus
Machine Learning in Montpellier, Theory & Practice – Martin Atzmüller (DFKI / Osnabrück University)
In many applications, modeling complex data is of utmost importance, requiring the use of advanced machine learning models and approaches. However, in many domains users require insight into models and/or their decisions, which is not necessarily provided by the respective models per se. Explainable and interpretable learning approaches can facilitate such insights for making sense of models and decisions. The talk presents examples of such approaches in complex modeling domains, including interpretable as well as explainable deep-learning-based methods, and a neuro-symbolic architecture including domain knowledge for facilitating explainability.
Machine Learning in Montpellier, Theory & Practice
Explainable and Interpretable Learning: Making Sense on Complex Modeling Domains
26 mars 2025 @ 14 h 30 min –
Room Nadir, Maison de la Télédétection (500 rue Jean François Breton)
Machine Learning in Montpellier, Theory & Practice – Martin Atzmüller, Scientific Director at DFKI and Full Professor at Osnabrück University (Germany)
In many applications, modeling complex data is of utmost importance, requiring the use of advanced machine learning models and approaches. However, in many domains users require insight into models and/or their decisions, which is not necessarily provided by the respective models per se. Explainable and interpretable learning approaches can facilitate such insights for making sense of models and decisions. The talk presents examples of such approaches in complex modeling domains, including interpretable as well as explainable deep-learning-based methods, and a neuro-symbolic architecture including domain knowledge for facilitating explainability.
LabéliséHallesIA