Symmetry and Asymmetry in Machine Learning: 2nd Edition
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 374
Special Issue Editors
Interests: neural networks; deep learning; machine learning; computer vision; natural language processing; stochastic optimization
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: neural networks; deep learning; machine learning; computer vision; natural language processing; stochastic optimization
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning focuses on the design and analysis of algorithms that enable computers to learn autonomously. It is widely applied across many fields, including image recognition, speech recognition, natural language processing, recommendation systems, classification, and prediction. This Special Issue aims to provide a platform for researchers to share their latest advances in neural networks and deep learning, as well as studies on the relationship between machine learning and symmetry and their applications to solving real-world problems.
Topics of interest for this Special Issue include, but are not limited to, the following:
- Symmetry and asymmetry in novel architectures and algorithms for machine learning;
- Faster and more robust methods for training deep models;
- Advances in fuzzy neural networks, spiking neural networks, extreme learning machines, and support vector machines;
- Machine learning applications in computer vision, speech recognition, natural language processing, and robotics;
- Theoretical analysis of neural networks;
- Transfer learning for deep learning systems;
- Deep neural network optimization and regularization technology;
- Deep learning for data analysis and prediction;
- Adversarial machine learning and its applications;
- Meta-learning and ensemble learning;
- Symmetric and asymmetric neural networks.
We invite researchers to submit original research articles, reviews, and short communications related to the above topics. All submissions will undergo a rigorous peer review process, and accepted papers will be published in this Special Issue of Symmetry.
Dr. Qinwei Fan
Dr. Jie Yang
Prof. Dr. Dongpo Xu
Guest Editors
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 submissions that pass pre-check are 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 250 words) can be sent to the Editorial Office for assessment.
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. Symmetry 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 2400 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.
Keywords
- machine learning
- deep learning
- convolutional neural networks
- spiking neural network
- recurrent neural networks
- graph neural network
- long short-term memory
- extreme learning machine
- generative adversarial networks
- reinforcement learning
- clustering analysis
- computer vision
- natural language processing
- time series analysis
- model-based clustering modeling high-dimensional
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