Initialization Scheme

Initialization schemes, crucial for the effective training of various machine learning models, aim to provide starting parameter values that promote faster convergence and improved performance. Current research focuses on optimizing initialization for diverse architectures, including neural networks (e.g., MLPs, CNNs, Transformers, and Neural ODEs), language models, and subspace encoders, often leveraging concepts like emergence and stability analysis to guide the process. Improved initialization strategies can significantly impact model accuracy, training speed, and robustness, leading to more efficient and effective machine learning across numerous applications.