Kellian Cottart

Kellian Cottart

Doctoral student in Bayesian AI and Neuromorphic engineering at Université Paris-Saclay | Data Sciences at Ecole Polytechnique (IPP2023) | Engineer's degree at Ecole Centrale d'Electronique

Talks

Neuromorphic Symposium: Bayesian Continual Learning and Forgetting in Neural Networks (15 minutes)

2 minute read

September 24, 2025

Talk, Neuromorphic Symposium, Paris, France

When neural networks are deployed in the real world, data does not stay stationary. Environments evolve, tasks change, and distributions drift. Standard neural networks suffer from catastrophic forgetting: when learning something new, they overwrite what they learned before. But there is a second, often ignored challenge: catastrophic remembering, when networks become too rigid, stop adapting, and lose their ability to fit new data, leading to overconfident predictions.

UQ4ML / COMETA Workshop: Bayesian Continual Learning and Forgetting in Neural Networks (30 minutes)

1 minute read

September 17, 2025

Talk, UQ4ML | COMETA Workshop on Uncertainty Quantification for Machine Learning, CEA Paris-Saclay, France

At the UQ4ML / COMETA Workshop, the talk Bayesian Continual Learning and Forgetting in Neural Networks presents recent work by Bonnet et al. introducing MESU (Metaplasticity from Synaptic Uncertainty), a Bayesian approach to continual learning that tackles both catastrophic forgetting and catastrophic remembering.