Recent Advances in Non-convex Smoothness Conditions and Applicability to Deep Linear Neural Networks
arXiv:2409.13672v1h-index: 2
Originality Synthesis-oriented
AI Analysis
This work provides a theoretical framework for analyzing convergence in deep learning, but it is incremental as it focuses on reviewing and applying existing conditions.
The paper reviews and orders new smoothness conditions for non-convex optimization in deep learning, applying them to train a deep linear neural network for binary classification.
The presence of non-convexity in smooth optimization problems arising from deep learning have sparked new smoothness conditions in the literature and corresponding convergence analyses. We discuss these smoothness conditions, order them, provide conditions for determining whether they hold, and evaluate their applicability to training a deep linear neural network for binary classification.