in this paper we turn the attention to a well developed theory of fuzzy/lin-guis-tic models that are interpretable and, moreover, can be learned from the data.we present four different situations demonstrating both interpretability as well as learning abilities of these models.

this paper considers the generation of some interpretable fuzzy rules for assigning an amino acid sequence into the appropriate protein superfamily. since the main objective of this classifier is the interpretability of rules, we have used the distribution of amino acids in the sequences of proteins as features. these features are the occurrence probabilities of six exchange groups in the seque...

Objective. This study aims to assess the impact of adaptive statistical iterative reconstruction (ASIR) on CT imaging quality, diagnostic interpretability, and radiation dose reduction for a proven CT acquisition protocol for total body trauma. Methods. 18 patients with multiple trauma (ISS ≥ 16) were examined either with a routine protocol (n = 6), 30% (n = 6), or 40% (n = 6) of iterative reco...

The independence results in arithmetic and set theory led to a proliferation of mathematical systems. One very general way to investigate the space of possible mathematical systems is under the relation of interpretability. Under this relation the space of possible mathematical systems forms an intricate hierarchy of increasingly strong systems. Large cardinal axioms provide a canonical means o...

Recent NLP literature has seen growing interest in improving model interpretability. Along this direction, we propose a trainable neural network layer that learns global interaction graph between words and then selects more informative using the learned word interactions. Our layer, call WIGRAPH, can plug into any network-based text classifiers right after its embedding layer. Across multiple S...

Deep operator networks (DeepONets) are powerful architectures for fast and accurate emulation of complex dynamics. As their remarkable generalization capabilities primarily enabled by projection-based attribute, we investigate connections with low-rank techniques derived from the singular value decomposition (SVD). We demonstrate that some concepts behind proper orthogonal (POD)-neural can impr...

Deep neural networks have been well-known for their superb handling of various machine learning and artificial intelligence tasks. However, due to over-parameterized black-box nature, it is often difficult understand the prediction results deep models. In recent years, many interpretation tools proposed explain or reveal how models make decisions. this paper, we review line research try a compr...

Neural networks for NLP are becoming increasingly complex and widespread, there is a growing concern if these models responsible to use. Explaining helps address the safety ethical concerns essential accountability. Interpretability serves provide explanations in terms that understandable humans. Additionally, post-hoc methods after model learned generally model-agnostic. This survey provides c...