2 papers
cs.LG2026
Reducing Memorisation in Generative Models via Riemannian Bayesian Inference
Johanna Marie Gegenfurtner, Albert Kjøller Jacobsen, Naima Elosegui Borras +2
Modern generative models can produce realistic samples, however, balancing memorisation and generalisation remains an open problem. We approach this challenge from a Bayesian persp…
cs.LG2025
Optimizers Qualitatively Alter Solutions And We Should Leverage This
Razvan Pascanu, Clare Lyle, Ionut-Vlad Modoranu +6
Due to the nonlinear nature of Deep Neural Networks (DNNs), one can not guarantee convergence to a unique global minimum of the loss when using optimizers relying only on local inf…