From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems
This addresses inefficiencies in training machine learning models for communication systems, but it is an incremental application of existing meta-learning concepts to a specific domain.
The paper introduces meta-learning as a method to reduce training data and time complexity in communication systems by automating the selection of inductive biases, enabling more efficient adaptation to new tasks without retraining.
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.