Neural Architecture Search for Energy Efficient Always-on Audio Models. - Daniel T. Speckhard; Karolis Misiunas; Sagi Perel; Tenghui Zhu; Simon Carlile; Malcolm Slaney (CoRR, 2022)

Validity Constraints for Data Analysis Workflows. - Florian Schintke; Ninon De Mecquenem; David Frantz; Vanessa Emanuela Guarino; Marcus Hilbrich; Fabian Lehmann; Rebecca Sattler; Jan Arne Sparka; Daniel T. Speckhard; Hermann Stolte; Anh Duc Vu 001; Ulf Leser (CoRR, 2023)

Band-gap regression with architecture-optimized message-passing neural networks. - Tim Bechtel; Daniel T. Speckhard; Jonathan Godwin; Claudia Draxl (CoRR, 2023)

excitingtools: An exciting Workflow Tool. - Alexander Buccheri; Fabian Peschel; Benedikt Maurer; Mara Voiculescu; Daniel T. Speckhard; Hannah Kleine; Elisa Stephan; Martin Kuban; Claudia Draxl (J. Open Source Softw., 2023)

Neural architecture search for energy-efficient always-on audio machine learning. - Daniel T. Speckhard; Karolis Misiunas; Sagi Perel; Tenghui Zhu; Simon Carlile; Malcolm Slaney (Neural Comput. Appl., 2023)

How big is Big Data? - Daniel T. Speckhard; Tim Bechtel; Luca M. Ghiringhelli; Martin Kuban; Santiago Rigamonti; Claudia Draxl (CoRR, 2024)

Validity constraints for data analysis workflows. - Florian Schintke; Khalid Belhajjame; Ninon De Mecquenem; David Frantz; Vanessa Emanuela Guarino; Marcus Hilbrich; Fabian Lehmann; Paolo Missier; Rebecca Sattler; Jan Arne Sparka; Daniel T. Speckhard; Hermann Stolte; et al. (Future Gener. Comput. Syst., 2024)

Analysis of static and dynamic batching algorithms for graph neural networks. - Daniel T. Speckhard; Tim Bechtel; Sebastian Kehl; Jonathan Godwin; Claudia Draxl (CoRR, 2025)

Grid Topology Estimation using an Information Theoretic Approach. - Daniel T. Speckhard (CoRR, 2025)