Choosing the values of hyper-parameters in sparse Bayesian learning (SBL) can significantly impact performance. However, the hyper-parameters are normally tuned manually, which is often a difficult task. Most recently, effective automatic hyper-parameter tuning was achieved by using an empirical auto-tuner. In this work, we address the issue of hyper-parameter auto-tuning using neural network (NN)-based learning. Inspired by the empirical auto-tuner, we design and learn a NN-based auto-tuner, and show that considerable improvement in convergence rate and recovery performance can be achieved.
翻译:在稀有的贝叶斯人学习(SBL)中选择超参数的值会大大影响性能。然而,超参数通常是手工调整的,这往往是一项困难的任务。最近,通过使用经验型自动教学,实现了有效的自动超参数调整。在这项工作中,我们讨论了使用神经网络(NN)的学习进行超参数自动调整的问题。在经验型自动教学的启发下,我们设计并学习了一个基于NNN的自动教学,并表明在趋同率和回收性能方面可以取得相当大的改进。