# Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning Source: [Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:27:36.356Z Authors: Konstantinos Kalais, Sotirios Chatzis Published: 2022-08-02T16:19:54.000Z ## Abstract 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 of sparse stochastic nature, as opposed to the currently standard deterministic representation paradigm. Our approach produces state-of-the-art predictive accuracy on few-shot image classification and regression experiments, as well as reduced predictive error on an active learning setting; these improvements come with an immensely reduced computational cost. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()