Automatic Gradient Descent

Automatic gradient descent (AGD) aims to eliminate the need for manual hyperparameter tuning in training neural networks, focusing on methods that automatically determine optimal learning rates and even network architectures. Current research explores Hessian-informed approaches like generalized Newton methods and architecture-aware optimizers that leverage network structure to improve convergence speed and performance, often achieving state-of-the-art results without extensive hyperparameter searches. This research significantly impacts deep learning by potentially simplifying the training process, reducing computational costs, and improving the robustness and generalizability of trained models across various applications.