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Leveraging Generative AI for Enhancing Domain-Driven Software Design
Published in Proceedings of the Upper-Rhine Artificial Intelligence Symposium (URAI 2024), 2024
Demonstrates that a 4-bit quantized Code Llama fine-tuned with LoRA on a consumer GPU can generate syntactically correct domain-specific JSON objects for Domain-Driven Design, partially automating metamodel creation.
Recommended citation: Wiegand, G.-H., Stepniak, F., & Baier, P. (2024). "Leveraging Generative AI for Enhancing Domain-Driven Software Design." Proceedings of the Upper-Rhine Artificial Intelligence Symposium, 2024, 41–50.
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A Convexity-Dependent Two-Phase Training Algorithm for Deep Neural Networks
Published in Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K KDIR 2025), 2025
Proposes a two-phase optimization algorithm that detects when the loss landscape transitions from non-convex to convex regions, switching from Adam to Conjugate Gradient to substantially improve convergence speed and accuracy.
Recommended citation: Hrycej, T., Bermeitinger, B., Pavone, M., Wiegand, G.-H., & Handschuh, S. (2025). "A Convexity-Dependent Two-Phase Training Algorithm for Deep Neural Networks." Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 78–86.
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