Data-Driven Artificial Intelligence, Optimization and Applications

Dear Colleagues,

As a cornerstone of modern scientific and industrial innovation, data-driven artificial intelligence has fostered the evolution of large-scale models, including large language models (LLMs), generative AI, and physical AI, which effectively exploit large-scale, multi-modal, and high-dimensional data to achieve outstanding performance in understanding, reasoning, and decision-making across diverse real-world scenarios. This Special Issue will focus on advanced theoretical and computational studies of data-driven AI, with a special attention on statistical theory, optimization, models, and applications.

The scope of this Special Issue encompasses a wide range of topics, including, but not limited to, the following:

1. Machine learning, statistical learning and optimization;
2. Theoretical foundations of deep learning;
3. Advanced neural architecture: theory, modeling, and applications;
4. Generative artificial intelligence and large-scale language models;
5. Explainable artificial intelligence models;
6. Transfer learning and continual learning algorithms;
7. Few-shot learning and out-of-distribution generalization;
8. Probabilistic models and Bayesian reasoning;
9. Compression, distillation, and inference acceleration of large-scale models;
10. Reinforcement learning and sequential decision-making;
11. Completion, enhancement, and restoration of degraded data;
12. Representation, fusion, and processing of high-dimensional multimodal data;
13. Intelligent perception systems: object detection, classification, segmentation, and tracking;
14. Agents, embodied intelligence, and intelligent robotics;
15. Advanced machine learning applications in fintech, healthcare, e-commerce, recommendation, and smart manufacturing.

Dr. Yi Xu
Guest Editor

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