Weight Tied Model

Weight-tied models, which share weights across multiple layers, offer a computationally efficient approach to training very deep neural networks, including the popular Deep Equilibrium Models (DEQs). Current research focuses on understanding the optimization properties of these models, particularly concerning convergence guarantees and the trade-off between model capacity and sparsity. This line of research aims to improve the stability and efficiency of training extremely deep networks while maintaining or improving performance on various tasks, such as image recognition, ultimately leading to more practical and scalable deep learning solutions.