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Optimization of Sparsity-Constrained Neural Networks as a Mixed Integer Linear Program: NN2MILP

Rosenhahn, Bodo (Dordrecht [u.a.] : Springer Science + Business Media, 2023)

The literature has shown how to optimize and analyze the parameters of different types of neural networks using mixed integer linear programs (MILP). Building on these developments, this work presents an approach to do so ...
Towards predicting immersion in surround sound music reproduction from sound field features

Kiyan, Roman; Bergner, Jakob; Preihs, Stephan; Wycisk, Yves; Schössow, Daphne; Sander, Kilian; Peissig, Jürgen; Kopiez, Reinhard (Les Ulis : EDP Sciences, 2023)

When evaluating surround sound loudspeaker reproduction, perceptual effects are commonly analyzed in relationship to different loudspeaker configurations. The presented work contributes to this by modeling perceptual effects ...
Using causal inference to avoid fallouts in data-driven parametric analysis: A case study in the architecture, engineering, and construction industry

Chen, Xia; Sun, Ruiji; Saluz, Ueli; Schiavon, Stefano; Geyer, Philipp ([Amsterdam] : Elsevier ScienceDirect, 2024)

The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models. Recognizing the limitations of isolated methodologies - namely, the lack of domain understanding in ...
Wear curve based online feature assessment for tool condition monitoring

Denkena, Berend; Bergmann, Benjamin; Stiehl, Tobias H. (Amsterdam : Elsevier B.V., 2020)

The performance of a process monitoring system is determined by the information available to it. Existing methods for selecting relevant process information (features) work offline with data of faulty processes that is ...

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Browsing by Subject "Feature selection"

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