Symmetry and Asymmetry in Machine Learning
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 18548
Special Issue Editors
Interests: neural networks; deep learning; machine learning; computer vision; natural language processing; stochastic optimization
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 mainly designs and analyzes algorithms that allow computers to learn autonomously. It is widely used in various fields, such as image recognition, speech recognition, natural language processing, recommendation systems, classification, prediction, etc. This Special Issue aims to provide a platform for researchers to share their latest advances in neural networks, and deep learning, and the correlation between machine learning and symmetry as well as 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 new architectures and algorithms for machine learning;
- Faster and more robust methods for the learning of 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;
- Neural network theory analysis;
- 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 networks/asymmetric networks.
We invite researchers to submit their 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 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. 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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