Randomly Initialized

Randomly initialized neural networks are a focus of current research, investigating how networks with initially random weights can achieve surprisingly good performance, even without training. Studies explore this phenomenon across various architectures, including convolutional and recurrent networks, focusing on understanding the underlying mechanisms through analyses of weight distributions, pruning techniques, and the relationship between overparameterization and generalization. This research aims to improve our understanding of deep learning's fundamental principles, potentially leading to more efficient training methods and more robust, compact models for various applications.