论文标题
从学习到元学习：减少培训开销和通信系统的复杂性
From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems
论文作者
论文摘要
机器学习方法通过基于数据或主动观察结果使用固定的学习过程来调整模型的参数，该参数被限制在给定的模型类中。适应性是按任务进行的，并且在系统配置更改时需要进行重新修复。通过选择合适的模型类别和学习过程（共同称为归纳偏见），可以减少在数据和培训时间要求方面的效率低下。但是，通常很难将先验知识编码为归纳偏差，尤其是在黑框模型类（例如神经网络）的情况下。元学习提供了一种自动选择电感偏差的方法。元学习利用与未来有关的任务和未知的事务的任务中利用数据或主动观察。凭借元训练的电感偏差，可以通过降低的训练数据和/或时间复杂性来进行机器学习模型的训练。本文为元学习提供了高级介绍，并提供了通信系统的应用。
Machine learning methods adapt the parameters of a model, constrained to lie in a given model class, by using a fixed learning procedure based on data or active observations. Adaptation is done on a per-task basis, and retraining is needed when the system configuration changes. The resulting inefficiency in terms of data and training time requirements can be mitigated, if domain knowledge is available, by selecting a suitable model class and learning procedure, collectively known as inductive bias. However, it is generally difficult to encode prior knowledge into an inductive bias, particularly with black-box model classes such as neural networks. Meta-learning provides a way to automatize the selection of an inductive bias. Meta-learning leverages data or active observations from tasks that are expected to be related to future, and a priori unknown, tasks of interest. With a meta-trained inductive bias, training of a machine learning model can be potentially carried out with reduced training data and/or time complexity. This paper provides a high-level introduction to meta-learning with applications to communication systems.
