Gradient Based Optimization

Gradient-based optimization is a cornerstone of modern machine learning, aiming to find optimal parameters for models by iteratively adjusting them along the direction of the gradient of a loss function. Current research focuses on addressing challenges such as escaping local minima in high-dimensional spaces (e.g., through hybridization with metaheuristics or novel learning rate strategies), improving the efficiency and stability of optimization for complex models like Mixture-of-Experts and Bayesian neural networks, and extending gradient-based methods to non-differentiable or discrete problems (e.g., using differentiable surrogates or novel gradient estimation techniques). These advancements are crucial for improving the performance, scalability, and robustness of machine learning models across diverse applications, from natural language processing and computer vision to material science and inverse problem solving.