Neural Network Initialization

Neural network initialization, the process of assigning initial values to network weights and biases, significantly impacts training speed and model performance. Current research focuses on developing data-driven and theoretically-grounded initialization methods, exploring techniques like leveraging derivative information, promoting emergence, and inducing sparsity in activations, often within the context of multilayer perceptrons, convolutional neural networks, recurrent neural networks, and transformers. These advancements aim to improve optimization efficiency, enhance generalization, and potentially reduce the need for extensive hyperparameter tuning, ultimately leading to more robust and effective deep learning models across various applications.