Gradient Flow Dynamic

Gradient flow dynamics analyze the behavior of neural network weights during training, aiming to understand how optimization algorithms like gradient descent lead to successful learning. Current research focuses on characterizing this behavior in various architectures, including multi-head attention models, two-homogeneous networks, and ReLU networks, often examining convergence properties and implicit biases towards specific solutions. These studies shed light on fundamental aspects of neural network training, such as the emergence of task allocation in multi-head attention and the role of saddle points in optimization landscapes, ultimately contributing to the development of more efficient and robust training methods.