Formal Analysis of Deep Artificial Neural Networks
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 3029
Special Issue Editors
Interests: artificial neural networks; pattern recognition; cluster analysis; statistical learning theory; data mining; multiple classifier systems; sensor fusion; affective computing
Special Issues, Collections and Topics in MDPI journals
Interests: artificial neural networks (ANN); probabilistic interpretation of ANNs; density estimation via ANNs; probabilistic graphical models for sequences; machine learning for graphs; combination of ANNs and graphical models
Special Issue Information
Dear Colleagues,
Artificial neural networks (ANN) represent a hot, fast-growing research field with a large impact on many application areas, such as computer vision, audio and video processing, bioinformatics, data science, pattern recognition, and natural language processing (to mention only a few). In spite of some long-established mathematical results on approximation and modeling capabilities of traditional ANN architectures (in particular, shallow ANNs), the theoretical understanding of the behavior and of the properties of complex or quantum ANNs (including very deep multilayer ANNs, or recurrent neural networks) is still limited. In recent years, the principles and methodologies of several mathematical disciplines have been proposed for the theoretical analysis of ANN architectures, dynamics, and learning. Such disciplines include approximation theory, complexity theory, information theory, as well as the study of von Neumann entropy in quantum ANNs. The purpose of this Special Issue is to highlight the state of the art of the investigation of ANNs in these contexts.
This Special Issue welcomes original research papers on the analysis of ANNs based on mathematically founded methods in general. Review articles describing the current state of the art of ANNs in the aforementioned contexts are highly encouraged. All submissions to this Special Issue must include substantial theoretical aspects of ANN research.
Dr. Friedhelm Schwenker
Dr. Edmondo Trentin
Guest Editors
Manuscript Submission Information
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Keywords
- ANN architectures and learning in approximation and complexity theories
- Cost functions and constraints in information-theoretic learning algorithms for ANNs
- Complexity of deep, recurrent, or quantum ANN learning
- Information-theoretic principles for sampling and feature extraction
- Analysis of learning based on information-theoretic methods (e.g., information bottleneck approach) in deep, recurrent, or quantum ANNs
- Applications of ANNs based on information-theoretic principles or quantum computing
- Theoretical advances in quantum ANNs
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