The varying coefficient models are very important tool to explore the dynamic pattern in many scientific areas, such as economics, finance, politics, epidemiology, medical science, ecology and so on. They are natural extensions of classical parametric models with good interpretability and are becoming more and more popular in data analysis. Thanks to their flexibility and interpretability, in t...

Automaton models are often seen as interpretable models. Interpretability itself is not well defined: it remains unclear what interpretability means without first explicitly specifying objectives or desired attributes. In this paper, we identify the key properties used to interpret automata and propose a modification of a state-merging approach to learn variants of finite state automata. We app...

Regulating deep neural networks (DNNs) with human structured knowledge has shown to be of great benefit for improved accuracy and interpretability. We develop a general framework that enables learning knowledge and its confidence jointly with the DNNs, so that the vast amount of fuzzy knowledge can be incorporated and automatically optimized with little manual efforts. We apply the framework to...

By attributing stand-alone phonetic interpretability to each melodic prime, we rid phonological output of segmental redundancy without compromising the ability of representations to be mapped onto articulation and auditory perception. Under this approach, the repertoire of constraints on segmental output is automatically restricted to those that refer to lexically distinctive primes. One specif...

This work describes a new methodology for fuzzy system modeling focused on maximizing the interpretability while keeping high accuracy. In order to get a good interpretability-accuracy trade-off, it considers the combination of both expert knowledge and knowledge extracted from data. Both types of knowledge are represented using the fuzzy logic formalism, in the form of linguistic variables and...

The inductive learning of a fuzzy rule-based classification system (FRBCS) with high interpretability is made difficult by the presence of a large number of features that increases the dimensionality of the problem being solved. The difficult comes from the exponential growth of the fuzzy rule search space with the increase in the number of features considered. In this paper we propose a geneti...

In this article we establish interpolation for the minimal system of interpretability logic IL. We prove that arrow interpolation holds for IL and that turnstile interpolation and interpolation for the-modality easily follow from this. Furthermore, these properties are extended to the system ILP. The related issue of Beth Deenability is also addressed. As usual, the arrow interpolation property...

The main objective of this work is to assess the impact of the initial conditions regarding interpretability and accuracy on the optimizer’s performance and present some guidelines in order to assist the designer. Automatic infusion of anesthesia is a task where both interpretability and accuracy of the controller are highly desirable features. Under this pretext, a description on how these goa...

From autonomous cars and adaptive email-filters to predictive policing systems, machine learning (ML) systems are increasingly ubiquitous; they outperform humans on specific tasks [Mnih et al., 2013, Silver et al., 2016, Hamill, 2017] and often guide processes of human understanding and decisions [Carton et al., 2016, Doshi-Velez et al., 2014]. The deployment of ML systems in complex applicatio...