Better Initialization

Better initialization techniques for neural networks are a crucial area of research aiming to improve model training efficiency and performance. Current efforts focus on leveraging pre-trained models (like foundation models and LLMs) to generate informative initializations, employing novel initialization schemes tailored to specific data types (e.g., tabular data or point clouds), and using meta-learning or Jacobian tuning for automated initialization optimization. These advancements lead to faster convergence, improved accuracy, and reduced computational costs across diverse applications, including active learning, neural architecture search, and various classification tasks.