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pro vyhledávání: '"Ostheimer, Phil"'
Topic models are a popular approach for extracting semantic information from large document collections. However, recent studies suggest that the topics generated by these models often do not align well with human intentions. While metadata such as l
Externí odkaz:
http://arxiv.org/abs/2410.18140
Autor:
Nagda, Mayank, Ostheimer, Phil, Specht, Thomas, Rhein, Frank, Jirasek, Fabian, Kloft, Marius, Fellenz, Sophie
Physics-Informed Neural Networks (PINNs) have emerged as a promising method for approximating solutions to partial differential equations (PDEs) using deep learning. However, PINNs, based on multilayer perceptrons (MLP), often employ point-wise predi
Externí odkaz:
http://arxiv.org/abs/2409.20206
Evaluating Text Style Transfer (TST) is a complex task due to its multifaceted nature. The quality of the generated text is measured based on challenging factors, such as style transfer accuracy, content preservation, and overall fluency. While human
Externí odkaz:
http://arxiv.org/abs/2308.13577
Text Style Transfer (TST) evaluation is, in practice, inconsistent. Therefore, we conduct a meta-analysis on human and automated TST evaluation and experimentation that thoroughly examines existing literature in the field. The meta-analysis reveals a
Externí odkaz:
http://arxiv.org/abs/2306.00539
Akademický článek
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