Gradient Based Optimisation

Gradient-based optimization is a core technique for training machine learning models, aiming to find model parameters that minimize a specified loss function. Current research focuses on addressing challenges posed by non-differentiable models and improving the efficiency and stability of optimization algorithms, including exploring novel approaches like preconditioning and incorporating curvature information, as well as investigating the implicit regularization effects of various optimization methods. These advancements are crucial for improving the performance and scalability of machine learning across diverse applications, from image recognition to robotics control, and for gaining a deeper theoretical understanding of deep learning's success.