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 …

When a channel model is not available, the end-to-end training of encoder and decoder on
a fading noisy channel generally requires the repeated use of the channel and of a feedback …

Deep learning has achieved remarkable success in many machine learning tasks such as
image classification, speech recognition, and game playing. However, these breakthroughs …

Typically, loss functions, regularization mechanisms and other important aspects of training
parametric models are chosen heuristically from a limited set of options. In this paper, we …

Meta-learning methods have shown an impressive ability to train models that rapidly learn
new tasks. However, these methods only aim to perform well in expectation over tasks …

Recent years have witnessed growing interest in the application of deep neural networks
(DNNs) for receiver design, which can potentially be applied in complex environments …

When a channel model is available, learning how to communicate on fading noisy channels
can be formulated as the (unsupervised) training of an autoencoder consisting of the …

Existing meta-learning works assume that each task has available training and testing data.
However, there are many available pre-trained models without accessing their training data …

Meta-learning, or learning to learn, is the science of systematically observing how different
machine learning approaches perform on a wide range of learning tasks, and then learning …

The ability to learn new concepts with small amounts of data is a critical aspect of
intelligence that has proven challenging for deep learning methods. Meta-learning has …