Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning. (arXiv:2208.01573v1 [cs.LG])

This work addresses meta-learning (ML) by considering deep networks with
stochastic local winner-takes-all (LWTA) activations. This type of network
units results in sparse representations from each model layer, as the units are
organized into blocks where only one unit generates a non-zero output. The main
operating principle of the introduced units rely on stochastic principles, as
the network performs posterior sampling over competing units to select the
winner. Therefore, the proposed networks are explicitly designed to extract
input data representations …

arxiv learning lg linear meta meta-learning model-agnostic networks stochastic