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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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Is More Data Worth the Cost? Dataset Scaling Laws in a Tiny Attention-Only Decoder
Published in IEEE SDS 2026 (Full Paper, Zürich) & ICLR 2026 Workshop on Data-FM (Rio de Janeiro, Brazil), 2026
Shows that dataset scaling laws hold even at small scales, where training on only 30% of data achieves about 90% of full performance in a tiny attention-only decoder.
Recommended citation: Wiegand, G.-H., Raichle, L., Staedeli, R., Handschuh, S., Hrycej, T., & Bermeitinger, B. (2026). "Is More Data Worth the Cost? Dataset Scaling Laws in a Tiny Attention-Only Decoder." IEEE International Conference on Data Science (SDS 2026), Zürich, Switzerland.