Process data based Anomaly detection in distributed energy generation using Neural Networks. - Max Klein; Gregor Thiele 001; Adalbert Fono; Niloufar Khorsandi; David Schade; Jörg Krüger (ICCAD, 2020)

Non-Computability of the Pseudoinverse on Digital Computers. - Holger Boche; Adalbert Fono; Gitta Kutyniok (CoRR, 2022)

Expressivity of Spiking Neural Networks. - Manjot Singh; Adalbert Fono; Gitta Kutyniok (CoRR, 2023)

Limitations of Deep Learning for Inverse Problems on Digital Hardware. - Holger Boche; Adalbert Fono; Gitta Kutyniok (IEEE Trans. Inf. Theory, 2023)

A Mathematical Framework for Computability Aspects of Algorithmic Transparency. - Holger Boche; Adalbert Fono; Gitta Kutyniok (ISIT, 2024)

Mathematical Algorithm Design for Deep Learning under Societal and Judicial Constraints: The Algorithmic Transparency Requirement. - Holger Boche; Adalbert Fono; Gitta Kutyniok (CoRR, 2024)

Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility through Quantization. - Holger Boche; Vít Fojtík; Adalbert Fono; Gitta Kutyniok (CoRR, 2024)

Sustainable AI: Mathematical Foundations of Spiking Neural Networks. - Adalbert Fono; Manjot Singh; Ernesto Araya; Philipp Christian Petersen; Holger Boche; Gitta Kutyniok (CoRR, 2025)

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time. - Duc Anh Nguyen; Ernesto Araya; Adalbert Fono; Gitta Kutyniok (CoRR, 2025)