Initial Weight

Initial weight assignment in neural networks significantly impacts training efficiency and model performance. Current research focuses on understanding how different weight initialization strategies, including random, sparse, low-rank, and even human-guided approaches, affect training dynamics, generalization, and the emergence of optimal subnetworks. This research spans various architectures, from deep equilibrium networks to transformers, and utilizes techniques like rank analysis and perturbation studies to uncover the underlying principles governing weight initialization's influence on model behavior. These findings are crucial for improving training speed, reducing computational costs, and enhancing the interpretability and robustness of neural networks.