Driven Initialization

Driven initialization focuses on improving the starting point of machine learning models, aiming to accelerate training and enhance performance by strategically selecting initial weights or parameters instead of relying solely on random initialization. Current research explores various approaches, including using quasirandom sequences, data-driven pretraining, and semantic-informed initialization for specific tasks, often applied to neural networks (e.g., Transformers, CNNs, and state space models) and reinforcement learning algorithms. These advancements offer significant potential for improving efficiency and accuracy across diverse applications, from image generation and medical diagnosis to serverless computing and neural architecture search.