Speaker
Description
The talk discusses the Abs-Normal Form as a structured representation of piecewise linear functions and examines its main advantages and limitations. This form provides a systematic way to capture piecewise linear behavior commonly encountered in Algorithmic Piecewise Differentiation. Alternative representations, including the ReLU Normal Form and the more general Piecewise Normal Form, are presented and compared. The discussion outlines conceptual connections between these forms and their potential to support future developments in algorithm design and analysis. The talk concludes with reflections on how these representations can be used more effectively and what directions may be important for advancing Algorithmic Piecewise Differentiation.