New Trends in Representation Learning
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 April 2026 | Viewed by 6
Special Issue Editors
Interests: machine learning; computer vision; multimedia
Interests: machine learning; representation learning
Special Issue Information
Dear Colleagues,
Representation learning stands at the forefront of artificial intelligence research, driving paradigm shifts across domains ranging from natural language processing to biomedical informatics. The rapid evolution of self-supervised learning, multimodal fusion, and geometric deep learning has unlocked unprecedented capabilities in extracting hierarchical patterns from complex data. However, fundamental challenges persist in scalability, interpretability, and cross-domain generalization, necessitating novel methodologies to bridge these gaps.
This Special Issue of Electronics seeks to explore cutting-edge advances that redefine how machines capture and utilize semantic, structural, and causal representations. We focus on architectures that transcend traditional feature engineering, enabling autonomous discovery of transferable knowledge across modalities and tasks—a capability that is critical for real-world applications in healthcare, robotics, and scientific discovery.
We invite original research and comprehensive reviews that address topics including (but not limited to) the following themes:
- Fundamental representation learning: self-supervised and contrastive learning, disentangled representation learning, and causal representation learning;
- Graph representation learning;
- Generative and adversarial representation learning;
- Efficient and lightweight representations;
- Representation learning for transfer learning and domain adaptation;
- Representation learning for interpretable machine learning;
- Representation learning for cross-modal learning;
- Representation learning in other domains, e.g., recommender systems, cybersecurity, natural language processing, and Large Language Models.
We look forward to receiving your contributions.
Dr. Mengmeng Jing
Dr. Liangjian Wen
Dr. Ye Li
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. Electronics is an international peer-reviewed open access semimonthly 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
- representation learning
- self-supervised learning
- transfer learning
- cross-modal learning
- interpretable representation learning
- graph representation learning
- causal representation learning
- adversarial learning
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
