Mathematical Methods for Deep Neural Network Optimization

Dear Colleagues,

This Special Issue will gather new theoretical insights and rigorous mathematical approaches for understanding and advancing the optimization of deep neural networks. Despite their success across various applications, deep learning models often rely on heuristic optimization strategies whose mathematical underpinnings remain only partially understood. This Special Issue will highlight recent advances in optimization methods, convergence analysis, and stability and generalization guarantees and the interplay between numerical mathematics, probability, and deep learning theory.

We particularly welcome contributions that explore optimization under non-convex landscapes, stochastic and deterministic methods, variational approaches, sparse and low-rank techniques, dynamical system perspectives, and the role of regularization in shaping learning outcomes. Submissions with applications across science, engineering, and medicine that are grounded in strong mathematical and algorithmic foundations are also encouraged.

Dr. Iman Tabatabaei Ardekani
Guest Editor

Manuscript Submission Information

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