2 citations · 3 across the 3 of their papers we have counts for
6 papers
Efficient Message Passing for Partial Differential Equation Priors
Anna Kazachkova, Leonhard Hennicke, Rainer Schlosser +1
Prior information for real-world physical quantities is most elegantly expressed via partial differential equations (PDEs). In this paper, we propose a novel way to solve PDEs usin…
Direct Message Approximation (DMA): A Consistency-Based Framework for Tractable Approximate Inference on Factor Graphs
Ralf Herbrich, Rainer Schlosser, Jan Lemcke +11
Approximate message passing on factor graphs underlies two dominant families of probabilistic inference algorithms: expectation propagation (EP) and variational message passing (VM…
Approximate Message Passing for Bayesian Neural Networks
Romeo Sommerfeld, Christian Helms, Ralf Herbrich
Bayesian neural networks (BNNs) offer the potential for reliable uncertainty quantification and interpretability, which are critical for trustworthy AI in high-stakes domains. Howe…
Energy-Efficient Sampling Using Stochastic Magnetic Tunnel Junctions
Nicolas Alder, Shivam Nitin Kajale, Milin Tunsiricharoengul +2
(Pseudo)random sampling, a costly yet widely used method in (probabilistic) machine learning and Markov Chain Monte Carlo algorithms, remains unfeasible on a truly large scale due…
AI, Climate, and Regulation: From Data Centers to the AI Act
Kai Ebert, Nicolas Alder, Ralf Herbrich +1
We live in a world that is experiencing an unprecedented boom of AI applications that increasingly penetrate and enhance all sectors of private and public life, from education, med…
AI, Climate, and Transparency: Operationalizing and Improving the AI Act
Nicolas Alder, Kai Ebert, Ralf Herbrich +1
This paper critically examines the AI Act's provisions on climate-related transparency, highlighting significant gaps and challenges in its implementation. We identify key shortcom…