the applications of Gauss-Newton Optimization for Training Deep Neural Networks

Abstract

This paper investigates the application of Gauss-Newton optimization for training deep neural networks (DNNs), addressing the limitations of traditional optimization methods. While techniques such as stochastic gradient descent (SGD) are widely used, they often suffer from slow convergence and sensitivity to hyperparameters. The Gauss-Newton method, leveraging second-order derivative information, offers a more efficient alternative by approximating the Hessian matrix, enabling more informed parameter updates and faster convergence rates.

In this study, we implement the Gauss-Newton optimization framework within a convolutional neural network architecture and evaluate its performance on the CIFAR-10 dataset, which consists of 60,000 images across 10 classes. Our experiments demonstrate that this method significantly improves classification accuracy and reduces loss compared to traditional SGD.

We discuss recent advancements in optimization techniques from studies conducted in 2024 and 2025, which further contextualize our findings. Detailed experimental results, including comparisons of convergence speed and final performance metrics, are provided. The paper includes links to the code and dataset for reproducibility, encouraging further exploration of Gauss-Newton optimization in deep learning applications. Through this work, we aim to highlight the potential of advanced optimization techniques in enhancing the training efficiency of deep neural networks, paving the way for future innovations in the field.