Zobrazeno 1 - 10
of 146
pro vyhledávání: '"Lüdtke Stefan"'
Autor:
Marton, Sascha, Grams, Tim, Vogt, Florian, Lüdtke, Stefan, Bartelt, Christian, Stuckenschmidt, Heiner
Reinforcement learning (RL) has seen significant success across various domains, but its adoption is often limited by the black-box nature of neural network policies, making them difficult to interpret. In contrast, symbolic policies allow representi
Externí odkaz:
http://arxiv.org/abs/2408.08761
Autor:
Tschalzev, Andrej, Marton, Sascha, Lüdtke, Stefan, Bartelt, Christian, Stuckenschmidt, Heiner
Tabular data is prevalent in real-world machine learning applications, and new models for supervised learning of tabular data are frequently proposed. Comparative studies assessing the performance of models typically consist of model-centric evaluati
Externí odkaz:
http://arxiv.org/abs/2407.02112
Autor:
Tschalzev, Andrej, Nitschke, Paul, Kirchdorfer, Lukas, Lüdtke, Stefan, Bartelt, Christian, Stuckenschmidt, Heiner
Neural networks often assume independence among input data samples, disregarding correlations arising from inherent clustering patterns in real-world datasets (e.g., due to different sites or repeated measurements). Recently, mixed effects neural net
Externí odkaz:
http://arxiv.org/abs/2407.01115
Despite the success of deep learning for text and image data, tree-based ensemble models are still state-of-the-art for machine learning with heterogeneous tabular data. However, there is a significant need for tabular-specific gradient-based methods
Externí odkaz:
http://arxiv.org/abs/2309.17130
Rule learning approaches for knowledge graph completion are efficient, interpretable and competitive to purely neural models. The rule aggregation problem is concerned with finding one plausibility score for a candidate fact which was simultaneously
Externí odkaz:
http://arxiv.org/abs/2309.00306
Autor:
Lüdtke, Stefan, Pierce, Maria E.
The accurate assessment of fish stocks is crucial for sustainable fisheries management. However, existing statistical stock assessment models can have low forecast performance of relevant stock parameters like recruitment or spawning stock biomass, e
Externí odkaz:
http://arxiv.org/abs/2308.03403
Decision Trees (DTs) are commonly used for many machine learning tasks due to their high degree of interpretability. However, learning a DT from data is a difficult optimization problem, as it is non-convex and non-differentiable. Therefore, common a
Externí odkaz:
http://arxiv.org/abs/2305.03515
Autor:
Wilken, Nils, Cohausz, Lea, Schaum, Johannes, Lüdtke, Stefan, Bartelt, Christian, Stuckenschmidt, Heiner
Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios it is important that goal recognition algorithms can recognize goals of an obse
Externí odkaz:
http://arxiv.org/abs/2301.10571
An important feature of pervasive, intelligent assistance systems is the ability to dynamically adapt to the current needs of their users. Hence, it is critical for such systems to be able to recognize those goals and needs based on observations of t
Externí odkaz:
http://arxiv.org/abs/2301.05608
Publikováno v:
2022. Proceedings of the 7th International Workshop on Sensor-based Activity Recognition and Artificial Intelligence. Association for Computing Machinery, New York, NY, USA
The automatic, sensor-based assessment of challenging behavior of persons with dementia is an important task to support the selection of interventions. However, predicting behaviors like apathy and agitation is challenging due to the large inter- and
Externí odkaz:
http://arxiv.org/abs/2207.08816