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- University of KasselDE5 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- Humboldt-Universität zu BerlinDE4 papers
- University of FreiburgDE4 papers
- Laboratoire de Mathématiques de Bretagne AtlantiqueFR3 papers
- Ludwig-Maximilians-Universität MünchenDE3 papers
- Ostfalia University of Applied SciencesDE3 papers
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- Astrophysique, Instrumentation et ModélisationFR2 papers
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63 papers
Sliding-Window Reordering with Overlap Averaging: A Simple Time-Domain Augmentation for Multivariate Forecasting
Jafar Bakhshaliyev, Johannes Burchert, Niels Landwehr +1
Augmentation has become a central technique for improving deep forecasting models, but classification-style transformations tend to break the coherence between the look-back window…
The Future of Feedback: How Can AI Help Transform Feedback to Be More Engaging, Effective, and Scalable?
Jennifer Meyer, Olaf Köller, Thorben Jansen +33
With digital learning environments becoming more prevalent, the ease with which generative AI enables the scalable production of real-time, automated feedback holds the potential t…
HMAR: Hierarchical Masked Attention for Multi-Behaviour Recommendation
Shereen Elsayed, Ahmed Rashed, Lars Schmidt-Thieme
In the context of recommendation systems, addressing multi-behavioral user interactions has become vital for understanding the evolving user behavior. Recent models utilize techniq…
What is the dimension of your binary data? -- and how to compute it quickly
Tom Hanika, Tobias Hille
Dimensionality is an important aspect for analyzing and understanding (high-dimensional) data. In their 2006 ICDM paper Tatti et al. answered the question for a (interpretable) dim…
A Repository for Formal Contexts
Tom Hanika, Robert Jäschke
Data is always at the center of the theoretical development and investigation of the applicability of formal concept analysis. It is therefore not surprising that a large number of…
Instance-Level Safety-Aware Fidelity of Synthetic Data and Its Calibration
Chih-Hong Cheng, Paul Stöckel, Xingyu Zhao
Modeling and calibrating the fidelity of synthetic data is paramount in shaping the future of safe and reliable self-driving technology by offering a cost-effective and scalable al…