3 papers
cs.LG2026
Sampling Parallelism for Fast and Efficient Bayesian Learning
Asena Karolin Ãzdemir, Lars H. Heyen, Arvid Weyrauch +3
Machine learning models, and deep neural networks in particular, are increasingly deployed in risk-sensitive domains such as healthcare, environmental forecasting, and finance, whe…
physics.optics2026
Inverse Design of Optical Multilayer Thin Films using Robust Masked Diffusion Models
Jonas Schaible, Asena Karolin Ãzdemir, Charlotte Debus +5
Inverse design of optical multilayer stacks seeks to infer layer materials, thicknesses, and ordering from a desired target spectrum. It is a long-standing challenge due to the lar…
cs.LG2026
Bayesian Lottery Ticket Hypothesis
Nicholas Kuhn, Arvid Weyrauch, Lars Heyen +3
Bayesian neural networks (BNNs) are a useful tool for uncertainty quantification, but require substantially more computational resources than conventional neural networks. For non-…