Graph-based semi-supervised learning: A comprehensive review

Z Song, X Yang, Z Xu, I King - IEEE Transactions on Neural …, 2022 - ieeexplore.ieee.org
Semi-supervised learning (SSL) has tremendous value in practice due to the utilization of
both labeled and unlabelled data. An essential class of SSL methods, referred to as graph …

[HTML][HTML] Green learning: Introduction, examples and outlook

CCJ Kuo, AM Madni - Journal of Visual Communication and Image …, 2023 - Elsevier
Rapid advances in artificial intelligence (AI) in the last decade have been largely built upon
the wide applications of deep learning (DL). However, the high carbon footprint yielded by …

A metaverse: Taxonomy, components, applications, and open challenges

SM Park, YG Kim - IEEE access, 2022 - ieeexplore.ieee.org
Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is
based on the social value of Generation Z that online and offline selves are not different …

How attentive are graph attention networks?

S Brody, U Alon, E Yahav - ar** the connectome of the human brain using structural or functional connectivity has
become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph …

Are we learning yet? a meta review of evaluation failures across machine learning

T Liao, R Taori, ID Raji, L Schmidt - Thirty-fifth Conference on …, 2021 - openreview.net
Many subfields of machine learning share a common stumbling block: evaluation. Advances
in machine learning often evaporate under closer scrutiny or turn out to be less widely …