# Remote Multilinear Compressive Learning with Adaptive Compression

arXiv: https://arxiv.org/abs/2109.01184 · HTML: https://mlsanity.com/paper/2109.01184/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 2 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [Deep Learning for IoT Big Data and Streaming Analytics: A Survey](https://mlsanity.com/paper/1712.04301/) (2017)
- [Compressed Learning: A Deep Neural Network Approach](https://mlsanity.com/paper/1610.09615/) (2016)
- [Multilinear Compressive Learning](https://mlsanity.com/paper/1905.07481/) (2019)
- [Multilinear Compressive Learning with Prior Knowledge](https://mlsanity.com/paper/2002.07203/) (2020)
- [Performance Indicator in Multilinear Compressive Learning](https://mlsanity.com/paper/2009.10456/) (2020)
- [Compressed Domain Image Classification Using a Dynamic-Rate Neural Network](https://mlsanity.com/paper/1901.09983/) (2019)

## Built on by (most cited first, 1 of 1)

- [Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications](https://mlsanity.com/paper/2207.10155/) (2022)

## Abstract (arXiv)

Multilinear Compressive Learning (MCL) is an efficient signal acquisition and learning paradigm for multidimensional signals. The level of signal compression affects the detection or classification performance of a MCL model, with higher compression rates often associated with lower inference accuracy. However, higher compression rates are more amenable to a wider range of applications, especially those that require low operating bandwidth and minimal energy consumption such as Internet-of-Things (IoT) applications. Many communication protocols provide support for adaptive data transmission to maximize the throughput and minimize energy consumption. By developing compressive sensing and learning models that can operate with an adaptive compression rate, we can maximize the informational content throughput of the whole application. In this paper, we propose a novel optimization scheme that enables such a feature for MCL models. Our proposal enables practical implementation of adaptive compressive signal acquisition and inference systems. Experimental results demonstrated that the proposed approach can significantly reduce the amount of computations required during the training phase of remote learning systems but also improve the informational content throughput via adaptive-rate sensing.