Zobrazeno 1 - 10
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pro vyhledávání: '"KRAUSE, ANNA"'
Predicting the evolution of spatiotemporal physical systems from sparse and scattered observational data poses a significant challenge in various scientific domains. Traditional methods rely on dense grid-structured data, limiting their applicability
Externí odkaz:
http://arxiv.org/abs/2403.19570
Accurate vegetation models can produce further insights into the complex interaction between vegetation activity and ecosystem processes. Previous research has established that long-term trends and short-term variability of temperature and precipitat
Externí odkaz:
http://arxiv.org/abs/2403.18438
Modeling data obtained from dynamical systems has gained attention in recent years as a challenging task for machine learning models. Previous approaches assume the measurements to be distributed on a grid. However, for real-world applications like w
Externí odkaz:
http://arxiv.org/abs/2306.14511
Publikováno v:
Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14169, p. 438-455. Springer, Cham
Previous work on learning physical systems from data has focused on high-resolution grid-structured measurements. However, real-world knowledge of such systems (e.g. weather data) relies on sparsely scattered measuring stations. In this paper, we int
Externí odkaz:
http://arxiv.org/abs/2306.05805
Time series are ubiquitous and therefore inherently hard to analyze and ultimately to label or cluster. With the rise of the Internet of Things (IoT) and its smart devices, data is collected in large amounts any given second. The collected data is ri
Externí odkaz:
http://arxiv.org/abs/2207.03119
Recent estimates report that companies lose 5% of their revenue to occupational fraud. Since most medium-sized and large companies employ Enterprise Resource Planning (ERP) systems to track vast amounts of information regarding their business process
Externí odkaz:
http://arxiv.org/abs/2206.04460
Publikováno v:
In KI 2022: Advances in Artificial Intelligence (pp. 75-89). Springer International Publishing (2022)
Many physical processes such as weather phenomena or fluid mechanics are governed by partial differential equations (PDEs). Modelling such dynamical systems using Neural Networks is an active research field. However, current methods are still very li
Externí odkaz:
http://arxiv.org/abs/2111.07671
Autor:
Davidson, Padraig, Steininger, Michael, Lautenschlager, Florian, Krause, Anna, Hotho, Andreas
Sensor-equipped beehives allow monitoring the living conditions of bees. Machine learning models can use the data of such hives to learn behavioral patterns and find anomalous events. One type of event that is of particular interest to apiarists for
Externí odkaz:
http://arxiv.org/abs/2110.03945
Autor:
Reichardt, Charlotte, Brandt, Sabine, Bernhardt, Anja, Krause, Anna, Lindquist, Jonathan A., Weinert, Sönke, Geffers, Robert, Franz, Tobias, Kahlfuss, Sascha, Dudeck, Anne, Mathew, Akash, Rana, Rajiv, Isermann, Berend, Mertens, Peter R.
Publikováno v:
In Kidney International August 2024 106(2):241-257
Autor:
Steininger, Michael, Abel, Daniel, Ziegler, Katrin, Krause, Anna, Paeth, Heiko, Hotho, Andreas
Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation. Model output statistics (MOS) reduce these errors by fitting the mo
Externí odkaz:
http://arxiv.org/abs/2012.10394