In the last century, a huge multi–disciplinary scientific endeavor is devoted to answer the historical questions in understanding the brain functions. Among the statistical methods used for this purpose, brain decoding provides a tool to predict the mental state of a human subject based on the recorded brain signal. Brain decoding is widely applied in the contexts of brain–computer interfacing,...

We investigate the modal logic of interpretability over Peano arith metic PA Our main result is an extension of the arithmetical complete ness theorem for the interpretability logic ILM This extension concerns recursively enumerable sets of formulas of interpretability logic rather than single formulas As corollaries we obtain a uniform arithmetical completeness theorem for the interpretability...

The aim of this contribution is to point out the importance of interpretability of fuzzy rule-based systems. We try to approach this key property axiomatically, as a starting point, restricting to interpretability of linguistic variables (in Zadeh’s sense). The proposed approach is underlined with an intuitive example. Finally, the benefits of considering interpretability in design and tuning o...

Interpretability is often a major concern in machine learning. Although many authors agree with this statement, interpretability is often tackled with intuitive arguments, distinct (yet related) terms and heuristic quantifications. This short survey aims to clarify the concepts related to interpretability and emphasises the distinction between interpreting models and representations, as well as...

Fuzzy Logic Based Classification System usually comes with two contradictory requirements to the obtained model; namely Accuracy and Interpretability. It is often found that a fuzzy system with highest classification accuracy shows poorer interpretability and vice versa. This is also observed in the hybrid GA-PSO approaches presented in the previous chapter. IGA has reasonable interpretability ...

Abstract Deep Learning methods are well-known for their abilities, but interpretability keeps them out of high-stakes situations. This difficulty is addressed by recent model-agnostic that provide explanations after the training process. As a result, current guidelines’ requirement “interpretability from start” not met. such only useful as sanity check model has been trained. In an abstract sce...

The word Interpretability is becoming more and more frequent in the fuzzy literature. It is admitted as the main advantage of fuzzy systems and it should be given a main role in fuzzy modeling. However, although researchers talk a lot about Interpretability, it is even not clear what it really means. Understanding of fuzzy systems is a subjective task which strongly depends on the person (exper...