Meta-learning, or learning to learn, offers a principled framework for few-shot learning. It leverages data from multiple related learning tasks to infer an inductive bias that enables fast adaptation on a new task. The application of meta-learning was recently proposed for learning how to demodulate from few pilots. The idea is to use pilots received and stored for offline use from multiple devices in order to meta-learn an adaptation procedure with the aim of speeding up online training on new devices. Standard frequentist learning, which can yield relatively accurate "hard" classification decisions, is known to be poorly calibrated, particularly in the small-data regime. Poor calibration implies that the soft scores output by the demodulator are inaccurate estimates of the true probability of correct demodulation. In this work, we introduce the use of Bayesian meta-learning via variational inference for the purpose of obtaining well-calibrated few-pilot demodulators. In a Bayesian framework, each neural network weight is represented by a distribution, capturing epistemic uncertainty. Bayesian meta-learning optimizes over the prior distribution of the weights. The resulting Bayesian ensembles offer better calibrated soft decisions, at the computational cost of running multiple instances of the neural network for demodulation. Numerical results for single-input single-output Rayleigh fading channels with transmitter's non-linearities are provided that compare symbol error rate and expected calibration error for both frequentist and Bayesian meta-learning, illustrating how the latter is both more accurate and better-calibrated.
翻译:元学习, 或学习学习, 为少许学习提供了一个原则性框架。 它利用来自多个相关学习任务的数据, 推导出能够快速适应新任务的进化偏差。 最近提议应用元学习, 学习如何从几个试点中降调。 我们的想法是使用从多个设备接收和存储的试点项目, 用于从多功能中脱线使用, 目的是加速在新设备上加快在线培训。 标准常入学习, 它可以产生相对准确的“ 硬性” 分类决定, 众所周知, 特别是在小数据制度中, 校准不力。 低校准意味着, 降压器的软性仓储分数产出是对正确降级的真正概率的不准确估计。 在这项工作中, 我们采用Bayesian 元学习, 通过变动推导法, 目的是为了在新设备上调校正。 在Bayesian框架中, 每一个神经网络的权重都通过一个分布, 记录误差的缩缩缩。 贝斯在先前的柔性轨道上, 最精确的元学习最精确的内分校正, 提供了更精确的校正 。