# Maximal function pooling with applications

Source: [Maximal function pooling with applications](<https://www.sota2.com/research/paper/68fa57e11e209131ce9a9551>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T14:59:38.179Z

Authors: Wojciech Czaja, Weilin Li, Yiran Li, Mike Pekala

Published: 2021-03-01T20:30:04.000Z

## Abstract

Inspired by the Hardy-Littlewood maximal function, we propose a novel pooling strategy which is called maxfun pooling. It is presented both as a viable alternative to some of the most popular pooling functions, such as max pooling and average pooling, and as a way of interpolating between these two algorithms. We demonstrate the features of maxfun pooling with two applications: first in the context of convolutional sparse coding, and then for image classification.

## 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](<https://arxiv.org/abs/2103.01292>)

[PDF](<https://arxiv.org/pdf/2103.01292.pdf>)