Optimal Initialization in Depth: Lyapunov Initialization and Limit Theorems for Deep Leaky ReLU Networks
Researchers have developed a new method for initializing deep neural networks called Lyapunov initialization. This technique is based on a rigorous probabilistic analysis of deep Leaky ReLU networks, revealing that activation stability is governed by a parameter known as the Lyapunov exponent. The new method aims to keep this exponent at zero, ensuring maximum stability and leading to improved learning performance in empirical tests. AI
IMPACT Introduces a novel initialization technique that could improve training stability and performance for deep learning models.