Interpretability, Reproducibility, and Replicability - Adali, Tulay; Guido, Rodrigo Capobianco [UNESP]; Ho, Tin Kam; Muller, Klaus-Robert; Strother, Stephen (Institute of Electrical and Electronics Engineers (IEEE), 2022)
Most of the work we do in signal processing these days is data driven. The shift from the more traditional and model-driven approaches to those that are data driven has also underlined the importance of explainability of our solutions. Because most traditional signal processing approaches start with a number of modeling assumptions, they are comprehensible by the very nature of their construction. However, this is not necessarily the case when we choose to rely more heavily on the data and minimize modeling assumptions.