Theoretical Foundations and Applications of Deep Learning Techniques

Dear Colleagues, 

Deep learning has revolutionized artificial intelligence, enabling breakthroughs in tasks such as image recognition, natural language processing, and autonomous systems. By leveraging complex neural network architectures, deep learning uncovers patterns and representations in data that traditional methods often fail to capture.

This Topic, “Theoretical Foundations and Applications of Deep Learning Techniques”, explores both the theoretical foundations and practical applications of deep learning techniques. From a theoretical perspective, we welcome studies on mathematical frameworks, optimization strategies, novel architectures, and interpretability. These contributions aim to advance our understanding of the mechanisms driving the success of deep learning.

On the application side, we seek innovative research showcasing the use of deep learning across diverse fields, including healthcare, robotics, finance, and climate science. Submissions addressing scalability, computational efficiency, and generalization challenges are particularly encouraged.

We invite original research papers and reviews, providing a platform to advance both the science and real-world impact of deep learning. We look forward to receiving your contributions and fostering a rich dialog in this dynamic field.

Prof. Dr. Juan Ernesto Solanes Galbis
Prof. Dr. Adolfo Muñoz García
Topic Editors