Social learning algorithms provide models for the formation of opinions over social networks resulting from local reasoning and peer-to-peer exchanges. Interactions occur an underlying graph topology, which describes flow information among agents. To account drifting conditions in environment, this work adopts adaptive strategy, is able to track variations signal statistics. Among other results...

Feature selection is the process of choosing a subset of features from the original larger set that are related to a certain outcome, such as disease type, dose, income, and time to event. The use of feature selection procedures is almost compulsory and complex in biology and medicine because the generation of massive datasets is nowadays common for many stateof-the-art technologies such as tra...

Analysis of continuous variables sometimes proceeds by selecting individuals on the basis of extreme scores of a sample distribution and submitting only those extreme scores to further analysis. This sampling method is known as the extreme groups approach (EGA). EGA is often used to achieve greater statistical power in subsequent hypothesis tests. However, there are several largely unrecognized...

We provide a novel notion of what it means to be interpretable, looking past the usual association with human understanding. Our key insight is that interpretability is not an absolute concept and so we define it relative to a target model, which may or may not be a human. We define a framework that allows for comparing interpretable procedures by linking it to important practical aspects such ...

We examined underaccommodation in hospital medical charts, with a focus on interpretability. In Study 1 147 hospital doctors completed a questionnaire including interpretations of chart entries from their own or another specialty. Study 2 used interviews with 10 doctors to explore interpretations of the same charts and perceptions of the writers. Results indicated that participants interpreted ...

A construction is presented for generating sentences that satisfy a recursively enumerable set of interpretability properties. This construction is then used to prove three previously announced results concerning the lattice of local interpretability types of theories (also known as the Lattice of Chapters). The purpose of this paper is to prove three theorems originally announced without proof...

The provability logic of a theory T is the set of modal formulas, which under any arithmetical realization are provable in T . We slightly modify this notion by requiring the arithmetical realizations to come from a specified set Γ. We make an analogous modification for interpretability logics. We first studied provability logics with restricted realizations, and show that for various natural c...

Granular Computing is an emerging conceptual and computational paradigm for information processing, which concerns representation and processing of complex information entities called “information granules” arising from processes of data abstraction and knowledge derivation. Within Granular Computing, a prominent position is assumed by the “Theory of Fuzzy Information Granulation” (TFIG) whose ...

Importance prioritised coding is a principle aimed at improving the interpretability (or image content recognition) versus bit-rate performance of image coding systems. This can be achieved by (1) detecting and tracking image content or regions of interest (ROI) that are crucial to the interpretation of an image, and (2) compressing them in such a manner that enables ROIs to be encoded with hig...

Evaluating an explanation's faithfulness is desired for many reasons such as trust, interpretability and diagnosing the sources of model's errors. In this work, which focuses on NLI task, we introduce methodology Faithfulness-through-Counterfactuals, first generates a counterfactual hypothesis based logical predicates expressed in explanation, then evaluates if prediction consistent with that l...