Novel applications of Convolutional Neural Networks in the age of Transformers

Author:

Ersavas Tansel,Smith Martin A.,Mattick John S.

Abstract

AbstractConvolutional Neural Networks (CNNs) have been central to the Deep Learning revolution and played a key role in initiating the new age of Artificial Intelligence. However, in recent years newer architectures such as Transformers have dominated both research and practical applications. While CNNs still play critical roles in many of the newer developments such as Generative AI, they are far from being thoroughly understood and utilised to their full potential. Here we show that CNNs can recognise patterns in images with scattered pixels and can be used to analyse complex datasets by transforming them into pseudo images with minimal processing for any high dimensional dataset, representing a more general approach to the application of CNNs to datasets such as in molecular biology, text, and speech. We introduce a pipeline called DeepMapper, which allows analysis of very high dimensional datasets without intermediate filtering and dimension reduction, thus preserving the full texture of the data, enabling detection of small variations normally deemed ‘noise’. We demonstrate that DeepMapper can identify very small perturbations in large datasets with mostly random variables, and that it is superior in speed and on par in accuracy to prior work in processing large datasets with large numbers of features.

Funder

Australian Government Research Training Program Scholarship

Fonds de Recherche du Quebec Santé

University of New South Wales

Publisher

Springer Science and Business Media LLC

Reference57 articles.

1. Taylor, P. Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2020, with forecasts from 2021 to 2025. https://www.statista.com/statistics/871513/worldwide-data-created/ (2023).

2. Ghys, É. The butterfly effect. in The Proceedings of the 12th International Congress on Mathematical Education: Intellectual and attitudinal challenges, pp. 19–39 (Springer). (2015).

3. Jolliffe, I. T. Mathematical and statistical properties of sample principal components. Principal Component Analysis, pp. 29–61 (Springer). https://doi.org/10.1007/0-387-22440-8_3 (2002).

4. Landauer, R. The noise is the signal. Nature 392, 658–659. https://doi.org/10.1038/33551 (1998).

5. Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press). http://www.deeplearningbook.org (2016).

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3