Special Issue "Entropy Based Inference and Optimization in Machine Learning"
A special issue of Entropy (ISSN 1099-4300).
Deadline for manuscript submissions: 20 December 2019.
Many modern machine learning algorithms are deeply rooted in the principles of statistical and information physics. A prominent example is the method of Maximum Entropy and its relations to Bayesian inference and optimization. Entropy-based methods have found many applications in modern machine learning, ranging from natural language processing to the development of approximate algorithms for large-scale data analysis. This special issue aims to focus on recent advances in entropy-based methods for inference and optimization problems in machine learning. We welcome submissions making novel contributions to the subject, both foundational as well as applied.
Prof. Stephen Roberts
Dr. Stefan Zohren
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Method of Maximum Entropy
- Applications in Machine Learning
- Statistical Physics of Learning Algorithms
- Information Physics
- Bayesian Inference
- Bayesian Optimization
- Approximate Algorithms
- Large Scale Data Analysis
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Authors: Robert K. Niven, Markus Abel, Michael Schlegel and Steven H. Waldrip