From the 1 of 6 linked papers with an AI index.
6 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…
Few is different: deciphering many-body dynamics in mesoscopic quantum gases
Juergen Berges, Sandra Brandstetter, Jasmine Brewer +27
Emergent macroscopic descriptions of matter, such as hydrodynamics, are central to our description of complex physical systems across a wide spectrum of energy scales. The conventi…
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…
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-…
Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism
Deifilia Kieckhefen, Markus Götz, Lars H. Heyen +2
AI-based methods have revolutionized atmospheric forecasting, with recent successes in medium-range forecasting spurring the development of climate foundation models. Accurate mode…
Emergent interaction-driven elliptic flow of few fermionic atoms
Sandra Brandstetter, Philipp Lunt, Carl Heintze +8
Hydrodynamics provides a successful framework to effectively describe the dynamics of complex many-body systems ranging from subnuclear to cosmological scales by introducing macros…