Connectionists: Formal Analysis of Deep Artificial Neural Networks (Entropy SI)

[Edmondo Trentin]

Dear colleagues, dear friends,

The Entropy Journal (https://www.mdpi.com/journal/entropy) is currently
running a Special Isuue on

Formal Analysis of Deep Artificial Neural Networks
(https://www.mdpi.com/journal/entropy/special_issues/DANN)

Submission deadline:  31 May 2022

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.

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

Feel free to contact us: Edmondo Trentin <trentin at dii.unisi.it> or 
Friedhelm Schwenker <friedhelm.schwenker at uni-ulm.de> if you are interested
in submitting your work.

Best,
Friedhelm Schwenker
Edmondo Trentin


-----------------------------------------------
Edmondo Trentin, PhD
Dip. Ingegneria dell'Informazione e Scienze MM.
V. Roma, 56 - I-53100 Siena (Italy)
E-mail:   trentin at dii.unisi.it
Voice:    +39-0577-234636
Fax:      +39-0577-233602
WWW:      http://www.dii.unisi.it/~trentin/HomePage.html
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