Learning Dynamic

Learning dynamics research investigates how models evolve during training, aiming to understand and optimize the process of acquiring knowledge and skills. Current efforts focus on characterizing learning trajectories across various model architectures, including neural networks (both deep and shallow), and algorithms like stochastic gradient descent, analyzing the influence of hyperparameters and initialization strategies on performance and stability. This research is crucial for improving model efficiency, generalization, and robustness in diverse applications, from robotics and reinforcement learning to understanding fundamental aspects of neural network behavior and biological learning.