# Approximation Algorithms for Cascading Prediction Models

Source: [Approximation Algorithms for Cascading Prediction Models](<https://www.sota2.com/research/paper/68fa64121e209131cea028b7>)

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

Data updated: 2025-10-25T20:56:11.088Z

Authors: Matthew Streeter

Published: 2018-02-21T17:56:05.000Z

## Abstract

We present an approximation algorithm that takes a pool of pre-trained models as input and produces from it a cascaded model with similar accuracy but lower average-case cost. Applied to state-of-the-art ImageNet classification models, this yields up to a 2x reduction in floating point multiplications, and up to a 6x reduction in average-case memory I/O. The auto-generated cascades exhibit intuitive properties, such as using lower-resolution input for easier images and requiring higher prediction confidence when using a computationally cheaper model.

## 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/1802.07697>)

[PDF](<https://arxiv.org/pdf/1802.07697.pdf>)