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Domain Specific Modeling

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6 documents available in our comprehensive collection of Domain Specific Modeling resources. Find practical guides, tutorials, and documentation to enhance your knowledge.

Preview of Domain-Specific Languages
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Domain-Specific Languages (Warnke_Dissertation_2021.pdf)

1.5 MBTom WarnkeEN 215 pages
This thesis explores the potential of domain-specific languages (DSLs) in modeling and simulation, studying different DSL design and implementation techniques
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Preview of electronic edition @ iscram.org (open access)
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electronic edition @ iscram.org (open access) (220_Tsiporkova_etal2012.pdf)

452 KBOriginally created by Ping Zhang / modified by Bartel Van de WalleEN 5 pages
Tsiporkova et al.
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ссылка (2102.12092.pdf)

9.66 MBAditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, IlyaEN 20 pages
Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset, involving complex architectures,
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Preview of SPHINX-II for WSJ CSR Task
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SPHINX-II for WSJ CSR Task (H92-1080.pdf)

596 KBF. Alleva; H. Hon; X. Huang; M. Hwang; R. Rosenfeld; R. WeideEN 6 pages
The configuration used includes sex-dependent, semi-continuous, shared-distribution hidden Markov models and left context dependent between-word triphones. Issues not previously addressed were tackled in this application.
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Preview of Evaluation of Language Identification Methods Across 285 Languages
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Evaluation of Language Identification Methods Across 285 Languages (ecp17131021.pdf)

744 KBTommi Jauhiainen ; Krister Linden ; Heidi JauhiainenEN 9 pages
It introduces a unified notation for these methods and demonstrates that high performance in limited scenarios may not translate to diverse language sets. The study concludes with the HeLI method excelling for texts exceeding 25 characters.
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Preview of Deep Learning for Comm & Radar ID
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Deep Learning for Comm & Radar ID (deep-learning-for-comms-and-radar-white-paper.pdf)

3.05 MBEN 16 pages
Leveraging synthesized data for deep learning in communications and radar, enabling modulation ID and target classification.
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