From the 1 of 4 linked papers with an AI index.
4 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…
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-…
A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting
Nicholas Kiefer, Arvid Weyrauch, Muhammed Ãz +3
The current landscape in time-series forecasting is dominated by Transformer-based models. Their high parameter count and corresponding demand in computational resources pose a cha…