论文标题
稀疏贝叶斯学习的高参数自动调节
Hyper-Parameter Auto-Tuning for Sparse Bayesian Learning
论文作者
论文摘要
在稀疏的贝叶斯学习（SBL）中选择超参数的值可以显着影响性能。但是，超参数通常是手动调整的，这通常是一项艰巨的任务。最近，通过使用经验自动调节器来实现有效的自动高参数调整。在这项工作中，我们使用基于神经网络（NN）学习的高参数自动调节问题。受经验自动调节器的启发，我们设计和学习了基于NN的自动调节器，并表明可以实现融合率和恢复性能的可观提高。
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.
