2 citations · 2 across the 2 of their papers we have counts for
3 papers
BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Python
Lars Kühmichel, Jerry M. Huang, Valentin Pratz +11
Modern Bayesian inference involves a mixture of computational methods for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows.…
Stable Single-Pixel Contrastive Learning for Semantic and Geometric Tasks
Leonid Pogorelyuk, Niels Bracher, Aaron Verkleeren +2
We pilot a family of stable contrastive losses for learning pixel-level representations that jointly capture semantic and geometric information. Our approach maps each pixel of an…
Amortized Bayesian Multilevel Models
Daniel Habermann, Marvin Schmitt, Lars Kühmichel +3
Multilevel models (MLMs) are a central building block of the Bayesian workflow. They enable joint, interpretable modeling of data across hierarchical levels and provide a fully pro…