3 papers
cs.LG2026
SOAP-Bubbles: Structured Weight Uncertainty for Neural Networks
Adrian Robert Minut, Nico Daheim, Marco Miani +3
Structured weight-uncertainty can improve many aspects of deep learning, but it remains costly to estimate and difficult to implement. Here, we show that these issues can be addres…
cs.LG2025
Bayesian generative models can flag performance loss, bias, and out-of-distribution image content
Miguel López-Pérez, Marco Miani, Valery Naranjo +2
Generative models are popular for medical imaging tasks such as anomaly detection, feature extraction, data visualization, or image generation. Since they are parameterized by deep…
cs.LG2025
Reparameterization invariance in approximate Bayesian inference
Hrittik Roy, Marco Miani, Carl Henrik Ek +4
Current approximate posteriors in Bayesian neural networks (BNNs) exhibit a crucial limitation: they fail to maintain invariance under reparameterization, i.e. BNNs assign differen…