Zobrazeno 1 - 10
of 215
pro vyhledávání: '"Knoll, Christian"'
The Bethe free energy approximation provides an effective way for relaxing NP-hard problems of probabilistic inference. However, its accuracy depends on the model parameters and particularly degrades if a phase transition in the model occurs. In this
Externí odkaz:
http://arxiv.org/abs/2405.15514
The analysis of complex high-dimensional data is a common task in many domains, resulting in bespoke visual exploration tools. Expectations and practices of domain experts as users do not always align with visualization theory. In this paper, we repo
Externí odkaz:
http://arxiv.org/abs/2404.03965
Bayesian causal inference (BCI) naturally incorporates epistemic uncertainty about the true causal model into down-stream causal reasoning tasks by posterior averaging over causal models. However, this poses a tremendously hard computational problem
Externí odkaz:
http://arxiv.org/abs/2402.14781
Charts are used to communicate data visually, but designing an effective chart that a broad set of people can understand is challenging. Usually, we do not know whether a chart's intended message aligns with the message readers perceive. In this mixe
Externí odkaz:
http://arxiv.org/abs/2310.05752
Autor:
Koesten, Laura, Gregory, Kathleen, Schuster, Regina, Knoll, Christian, Davies, Sarah, Möller, Torsten
Data visualizations are used to communicate messages to diverse audiences. It is unclear whether interpretations of these visualizations match the messages their creators aim to convey. In a mixed-methods study, we investigate how data in the popular
Externí odkaz:
http://arxiv.org/abs/2304.10544
Autor:
Fuchs, Alexander, Knoll, Christian, Moghadam, Nima N., Huang, Alexey Pak Jinliang, Leitinger, Erik, Pernkopf, Franz
Multiple-Input Multiple-Output (MIMO) systems are essential for wireless communications. Sinceclassical algorithms for symbol detection in MIMO setups require large computational resourcesor provide poor results, data-driven algorithms are becoming m
Externí odkaz:
http://arxiv.org/abs/2303.07821
Autor:
Knoll, Christian
Probabilistic graphical models are a powerful concept for modeling high-dimensional distributions. Besides modeling distributions, probabilistic graphical models also provide an elegant framework for performing statistical inference; because of the h
Externí odkaz:
http://arxiv.org/abs/2209.05464
Autor:
Toth, Christian, Lorch, Lars, Knoll, Christian, Krause, Andreas, Pernkopf, Franz, Peharz, Robert, von Kügelgen, Julius
Causal discovery and causal reasoning are classically treated as separate and consecutive tasks: one first infers the causal graph, and then uses it to estimate causal effects of interventions. However, such a two-stage approach is uneconomical, espe
Externí odkaz:
http://arxiv.org/abs/2206.02063
Deep neural networks rely heavily on normalization methods to improve their performance and learning behavior. Although normalization methods spurred the development of increasingly deep and efficient architectures, they also increase the vulnerabili
Externí odkaz:
http://arxiv.org/abs/2110.01955
Publikováno v:
Eur. Phys. J. C 82, 533 (2022)
We present a discussion of the traversable wormholes in Einstein-Dirac-Maxwell theory recently reported in e-Print: 2010.07317. This includes a detailed description of the ansatz and junction condition, together with an investigation of the domain of
Externí odkaz:
http://arxiv.org/abs/2108.12187