“Gradient descent for training neural networks on microcontrollers” – David Josef Muttenthaler, 2025

“Reproducible data distribution changes for evaluating the adaptability of neural networks” – David Reinberger, 2025

“Collaborative training of machine learning models in heterogeneous microcontroller networks” – Matthias Köhler, 2025

“Optimization of an AI-based energy management system for embedded hardware: Porting symbolic-regressive models, performance enhancement, and retraining” – Alexander Prielinger, 2025

“Retraining of Neural Networks on Resource-Limited Devices” – Florian D. Meißl, 2024

P. Kastner, Doka Austria, Amstetten, “Smart construction site – Safe construction site”, 04.11.2025

F. Eibensteiner, Workshop on Embedded AI and Retraining, FH Hagenberg, 24.06.2025

N. Teringl, F. Eibensteiner, AI in quality control – flexible, intuitive, 100% tested, online lecture at the Austrian Chamber of Commerce, Linz, 16.06.2025

F. Eibensteiner, Adaptive AI Systems: The Next Stage of Evolution, presentation at the Embedded AI: Present & Future workshop, Danube Dynamics Embedded Solutions GmbH, Linz, May 27, 2025

F. Eibensteiner, Workshop on Embedded AI, FH Hagenberg, 12.03.2025

F. Eibensteiner, Retraining on Embedded Systems, Christian Doppler Laboratory for Embedded Machine Learning at the Institute of Computer Technology, TU Vienna, 29.11.2024

C. Dalpiaz, K. Preiner, M. Kargl, P. Kastner, F. Eibensteiner, J. Langer, „Optimization Potential for Adapting Symbolic Regression Models Applied to Energy Flow Control“, URBan SENSEmaking and Intelligence for Safer Cities (URBSENSE) 2026

C. Dalpiaz, K. Preiner, P. Kastner, M. Kargl, F. Eibensteiner, J. Langer, „Meta-modeling Power Inverters for Fast Evaluation of Energy Flow Controllers on Resource Limited Devices“, Eurocast 2026, 20th International Conference on Computer Aided Systems Theory

A. Prielinger, C. Dalpiaz, F. Eibensteiner, K. Preiner, J. Langer, „Optimization Strategies for Deploying Symbolic Regression Models on Embedded Hardware for Energy Management“, Eurocast 2026, 20th International Conference on Computer Aided Systems Theory

Edge to Edge: Enabling Comparative Benchmarking for On-Device Training with a TensorFlow Lite Baseline, M. Kargl, F. Eibensteiner, C. Dalpiaz, P. Kastner, J. Langer, International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) 2026

An Embedded On-Device Training Framework for Neural Networks, M. Kargl, D. Muttenthaler, F. Eibensteiner, C. Dalpiaz, P. Kastner, J. Langer, Eurocast 2026