Random Initialization

Random initialization, the process of assigning starting values to parameters in machine learning models, significantly impacts training efficiency and model performance. Current research focuses on developing improved initialization strategies for various architectures, including neural networks (deep linear, residual, and convolutional), matrix factorization, and clustering algorithms (K-means), often leveraging techniques like orthonormal constraints, binomial distributions, or alternative optimization methods such as mirror descent. These advancements aim to enhance convergence speed, improve solution quality, and reduce the reliance on computationally expensive pre-training, ultimately leading to more efficient and effective machine learning models across diverse applications.