论文标题
镜子下降的霍普菲尔德模型
Mirror descent of Hopfield model
论文作者
论文摘要
镜下降是一种优雅的优化技术，它利用参数模型的双空间进行梯度下降。虽然最初是用于凸优化的，但它越来越多地应用于机器学习领域。在这项研究中，我们提出了一种利用镜下下降来初始化神经网络参数的新方法。具体而言，我们证明，通过将Hopfield模型用作神经网络的原型，与依赖于随机参数初始化的传统梯度下降方法相比，Mirror Descent可以有效地训练该模型的性能。我们的发现突出了镜下降作为增强机器学习模型优化的有希望的初始化技术的潜力。
Mirror descent is an elegant optimization technique that leverages a dual space of parametric models to perform gradient descent. While originally developed for convex optimization, it has increasingly been applied in the field of machine learning. In this study, we propose a novel approach for utilizing mirror descent to initialize the parameters of neural networks. Specifically, we demonstrate that by using the Hopfield model as a prototype for neural networks, mirror descent can effectively train the model with significantly improved performance compared to traditional gradient descent methods that rely on random parameter initialization. Our findings highlight the potential of mirror descent as a promising initialization technique for enhancing the optimization of machine learning models.
