Zobrazeno 1 - 10
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pro vyhledávání: '"Marx Alexander"'
Autor:
Marx, Alexander, Epp, Sascha W.
The analysis of ultrafast electron diffraction (UED) data from low-symmetry single crystals of small molecules is often challenged by the difficulty of assigning unique Laue indices to the observed Bragg reflections. For a variety of technical and ph
Externí odkaz:
http://arxiv.org/abs/2412.04197
Anomaly detection focuses on identifying samples that deviate from the norm. When working with high-dimensional data such as images, a crucial requirement for detecting anomalous patterns is learning lower-dimensional representations that capture con
Externí odkaz:
http://arxiv.org/abs/2405.18848
The pointwise mutual information profile, or simply profile, is the distribution of pointwise mutual information for a given pair of random variables. One of its important properties is that its expected value is precisely the mutual information betw
Externí odkaz:
http://arxiv.org/abs/2310.10240
Posterior sampling allows exploitation of prior knowledge on the environment's transition dynamics to improve the sample efficiency of reinforcement learning. The prior is typically specified as a class of parametric distributions, the design of whic
Externí odkaz:
http://arxiv.org/abs/2310.07518
Mutual information is a general statistical dependency measure which has found applications in representation learning, causality, domain generalization and computational biology. However, mutual information estimators are typically evaluated on simp
Externí odkaz:
http://arxiv.org/abs/2306.11078
Contrastive learning is a cornerstone underlying recent progress in multi-view and multimodal learning, e.g., in representation learning with image/caption pairs. While its effectiveness is not yet fully understood, a line of recent work reveals that
Externí odkaz:
http://arxiv.org/abs/2303.09166
Autor:
Immer, Alexander, Schultheiss, Christoph, Vogt, Julia E., Schölkopf, Bernhard, Bühlmann, Peter, Marx, Alexander
We study the class of location-scale or heteroscedastic noise models (LSNMs), in which the effect $Y$ can be written as a function of the cause $X$ and a noise source $N$ independent of $X$, which may be scaled by a positive function $g$ over the cau
Externí odkaz:
http://arxiv.org/abs/2210.09054
Autor:
Marx, Alexander, Clasen, Anne, May, Johannes, König, Simon, Kleinschmit, Birgit, Förster, Michael
Publikováno v:
In International Journal of Applied Earth Observation and Geoinformation September 2024 133
Autor:
Marx, Alexander, Fischer, Jonas
Estimating mutual information (MI) between two continuous random variables $X$ and $Y$ allows to capture non-linear dependencies between them, non-parametrically. As such, MI estimation lies at the core of many data science applications. Yet, robustl
Externí odkaz:
http://arxiv.org/abs/2110.13883
Autor:
Marx, Alexander, Vreeken, Jilles
The algorithmic independence of conditionals, which postulates that the causal mechanism is algorithmically independent of the cause, has recently inspired many highly successful approaches to distinguish cause from effect given only observational da
Externí odkaz:
http://arxiv.org/abs/2105.01902