Robust and Reliable Neural Networks for Real-World Data
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 2081
Editor
Interests: big data; natural language processing; knowledge graph; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue focuses on the development and application of robust and reliable deep neural network (DNN) methodologies for real-world data, where noise, incompleteness, heterogeneity, and uncertainty are common challenges. It aims to highlight innovative approaches that enhance the performance, stability, and interpretability of neural network models across diverse application domains.
Topics of interest include, but are not limited to, the following:
- Techniques for handling noisy, corrupted, or incomplete labels in supervised learning;
- Robust training strategies, loss functions, and regularization methods for DNNs;
- Metadata-guided, semi-supervised, and weakly supervised approaches to improve model reliability;
- Adversarial robustness, uncertainty quantification, and generalization under distribution shifts;
- Applications in natural language processing (NLP), computer vision, bioinformatics, healthcare, and other real-world scenarios;
- Hybrid or ensemble methods, explainable AI, and interpretable deep learning models;
- Methods for generating realistic synthetic datasets to address Non-IID and out-of-distribution (OOD) spatiotemporal data, particularly for dynamic environments such as IoT, sensor networks, and cyber-physical systems.
This Special Issue encourages contributions integrating theoretical advancements, algorithmic innovations, and practical implementations to address the challenges posed by imperfect, complex, or non-stationary datasets. It provides a platform for researchers and practitioners to share insights, methods, and solutions that enhance the robustness, reliability, and real-world applicability of neural network systems.
Prof. Dr. Xue Li
Guest Editor
Manuscript Submission Information
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Keywords
- robust deep learning
- noisy and incomplete data
- uncertainty quantification
- generalization and distribution shift
- explainable and reliable AI
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