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Recent advances in trustworthy explainable artificial intelligence: Status, challenges, and perspectives
Artificial intelligence (AI) and machine learning (ML) have come a long way from the earlier
days of conceptual theories, to being an integral part of today's technological society. Rapid …
days of conceptual theories, to being an integral part of today's technological society. Rapid …
Neuro-symbolic artificial intelligence: a survey
The goal of the growing discipline of neuro-symbolic artificial intelligence (AI) is to develop
AI systems with more human-like reasoning capabilities by combining symbolic reasoning …
AI systems with more human-like reasoning capabilities by combining symbolic reasoning …
Logic tensor networks
Attempts at combining logic and neural networks into neurosymbolic approaches have been
on the increase in recent years. In a neurosymbolic system, symbolic knowledge assists …
on the increase in recent years. In a neurosymbolic system, symbolic knowledge assists …
Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning
Current advances in Artificial Intelligence and machine learning in general, and deep
learning in particular have reached unprecedented impact not only across research …
learning in particular have reached unprecedented impact not only across research …
Deep logic networks: Inserting and extracting knowledge from deep belief networks
Developments in deep learning have seen the use of layerwise unsupervised learning
combined with supervised learning for fine-tuning. With this layerwise approach, a deep …
combined with supervised learning for fine-tuning. With this layerwise approach, a deep …
Innovative second-generation wavelets construction with recurrent neural networks for solar radiation forecasting
Solar radiation prediction is an important challenge for the electrical engineer because it is
used to estimate the power developed by commercial photovoltaic modules. This paper …
used to estimate the power developed by commercial photovoltaic modules. This paper …
Bilateral sensitivity analysis: a better understanding of a neural network
A model-independent sensitivity analysis for (deep) neural network, Bilateral sensitivity
analysis (BiSA), is proposed to measure the relationship or dependency between neurons …
analysis (BiSA), is proposed to measure the relationship or dependency between neurons …
Controlling recurrent neural networks by conceptors
The human brain is a dynamical system whose extremely complex sensor-driven neural
processes give rise to conceptual, logical cognition. Understanding the interplay between …
processes give rise to conceptual, logical cognition. Understanding the interplay between …
An empirical evaluation of rule extraction from recurrent neural networks
Rule extraction from black box models is critical in domains that require model validation
before implementation, as can be the case in credit scoring and medical diagnosis. Though …
before implementation, as can be the case in credit scoring and medical diagnosis. Though …
Linear priors mined and integrated for transparency of blast furnace black-box SVM model
Black-box models are a kind of effective means to describe extremely complex systems,
such as blast furnaces. However, an evident deficiency for them lies in the lack of …
such as blast furnaces. However, an evident deficiency for them lies in the lack of …