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Generating CQQL Conditions from Classifying CNNs

  • Convolutional neural networks are often successfully used for clas- si cation problems. Usually, a huge number of weights need to be learnt by use of training data. However, the learnt weights give no insight how the cnn really works. Thus, a cnn can be seen as a black box solution. In our approach we develop a method to generate a commuting quantum query language (cqql) condition from a sample derived from a given cnn or from training input. The query language cqql is inspired by quantum logic and its conditions obey the rules of a Boolean algebra. The evaluation of a cqql condition provides values from the unit interval [0; 1] and establishes therefore an elegant bridge between logic and a cnn. The underlying assumption is that a condition (a logic expression) gives much more understanding than pure cnn weights. Furthermore, the rich theory of Boolean algebra can be used for manipulating logic expressions. After extracting a cqql condition from a cnn or its training data we can use logic as a way to predict classesConvolutional neural networks are often successfully used for clas- si cation problems. Usually, a huge number of weights need to be learnt by use of training data. However, the learnt weights give no insight how the cnn really works. Thus, a cnn can be seen as a black box solution. In our approach we develop a method to generate a commuting quantum query language (cqql) condition from a sample derived from a given cnn or from training input. The query language cqql is inspired by quantum logic and its conditions obey the rules of a Boolean algebra. The evaluation of a cqql condition provides values from the unit interval [0; 1] and establishes therefore an elegant bridge between logic and a cnn. The underlying assumption is that a condition (a logic expression) gives much more understanding than pure cnn weights. Furthermore, the rich theory of Boolean algebra can be used for manipulating logic expressions. After extracting a cqql condition from a cnn or its training data we can use logic as a way to predict classes alternatively to a cnn.show moreshow less

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Metadaten
Author: Ingo SchmittORCiDGND
URN:urn:nbn:de:kobv:co1-opus4-55503
DOI:https://doi.org/10.26127/BTUOpen-5550
ISSN:1437-7969
Place of publication:Cottbus
Document Type:Report
Language:English
Year of publication:2021
Tag:Abfragesprache; CNN; Erklärbarkeit; Quantenlogik; Zellulares neuronales Netzwerk
CNN; CQQL; Quantum Logic
Number of pages:15
Series ; volume number:Computer science reports / Institut für Informatik (2021, 1)
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Datenbank- und Informationssysteme
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