2 citations · 3 across the 4 of their papers we have counts for
5 papers
High-Dimensional Simulation-Based Inference in Latent Spaces
Lars Kühmichel, Stefan T. Radev, Bhanu Prasanna Koppolu +3
Neural simulation-based inference (SBI) has been widely successful in inferring a relatively small number of interpretable parameters from potentially high-dimensional observations…
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…
Towards Context-Aware Domain Generalization: Understanding the Benefits and Limits of Marginal Transfer Learning
Jens Müller, Lars Kühmichel, Martin Rohbeck +2
In this work, we analyze the conditions under which information about the context of an input can improve the predictions of deep learning models in new domains. Following work…