Integrating Machine Learning with Non-Fungible Tokens

Elias Iosif, L. Katelaris
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Abstract

In this paper, we undertake a thorough comparative examination of data resources pertinent to Non-Fungible Tokens (NFTs) within the framework of Machine Learning (ML). The core research question of the present work is how the integration of ML techniques and NFTs manifests across various domains. Our primary contribution lies in proposing a structured perspective for this analysis, encompassing a comprehensive array of criteria that collectively span the entire spectrum of NFT-related data. To demonstrate the application of the proposed perspective, we systematically survey a selection of indicative research works, drawing insights from diverse sources. By evaluating these data resources against established criteria, we aim to provide a nuanced understanding of their respective strengths, limitations, and potential applications within the intersection of NFTs and ML.
将机器学习与不可篡改的代币相结合
在本文中,我们在机器学习(ML)的框架内对与非风俗代币(NFT)相关的数据资源进行了全面的比较研究。这项工作的核心研究问题是,机器学习技术与 NFT 的整合如何在不同领域中体现出来。我们的主要贡献在于为这一分析提出了一个结构化的视角,其中包含一系列全面的标准,这些标准共同跨越了 NFT 相关数据的整个范围。为了展示所提观点的应用,我们系统地调查了一系列具有指示性的研究成果,从不同的来源汲取见解。通过根据既定标准对这些数据资源进行评估,我们旨在对它们各自的优势、局限性以及在 NFT 和 ML 交叉领域的潜在应用提供细致入微的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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