From the 1 of 12 linked papers with an AI index.
12 papers
Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance
Pei-Hsuan Hsia, Lars H. Heyen, Arvid Weyrauch +4
The paper investigates using a Student's t likelihood instead of a Gaussian in Bayesian neural networks and finds it improves predictive performance and sometimes reduces training…
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…
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…
Differentiable Power-Flow Optimization
Muhammed Ãz, Jasmin Hörter, Kaleb Phipps +3
With the rise of renewable energy sources and their high variability in generation, the management of power grids becomes increasingly complex and computationally demanding. Conven…
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-…
Feed-Forward Optimization With Delayed Feedback for Neural Network Training
Katharina Flügel, Daniel Coquelin, Marie Weiel +3
Backpropagation has long been criticized for being biologically implausible due to its reliance on concepts that are not viable in natural learning processes. Two core issues are t…