Versatility of Artificial Hydrocarbon Networks for Supervised Learning

Ponce, Hiram; Martínez-Villaseñor, María de Lourdes

doi:10.1007/978-3-030-02837-4_1

Cited by 3 publications

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“…Literature reports many different applications of AHN. Those can be classified as follows: function approximation and modeling [1]; robust human activity recognition systems [5]; signal processing in denoising audio and face recognition [1,6]; online advertising [6]; intelligent control systems for robotics [1,7,8,11] and mechatronics [1,9,10]; bio/medical applications [5,6,12]; and, theoretical approaches such as hybrid fuzzy-molecular inference systems [8], interpretability of the model [12] and training algorithms [3,4].…”

Section: Applications Of Artificial Hydrocarbon Networkmentioning

confidence: 99%

Challenges and Issues on Artificial Hydrocarbon Networks: The Chemical Nature of Data-Driven Approaches

Ponce¹

2019

LatinX in AI at International Conference on Machine Learning 2019

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Inspiration in nature has been widely explored, from macro to micro-scale. When looking into chemical phenomena, stability and organization are two properties that emerge. Recently, artificial hydrocarbon networks (AHN), a supervised learning method inspired in the inner structures and mechanisms of chemical compounds, have been proposed as a data-driven approach in artificial intelligence. AHN have been successfully applied in data-driven approaches, such as: regression and classification models, control systems, signal processing, and robotics. To do so, molecules –the basic units of information in AHN– play an important role in the stability, organization and interpretability of this method. Interpretability, saving computing resources, and predictability have been handled by AHN, as any other machine learning model. This short paper aims to highlight the challenges, issues and trends of artificial hydrocarbon networks as a data-driven method. Throughout this document, it presents a description of the main insights of AHN and the efforts to tackle interpretability and training acceleration. Potential applications and future trends on AHN are also discussed.

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