Many real-world datasets, ranging from web-interaction logs to biomedical networks, can be represented as sparse tensors where most entries are unobserved. Tensor factorization (TF) learns latent representations and a predictor to estimate unobserved entries and has been widely applied to higher-order recommendation, b...
Jun-Gi Jang, Jingrui He, A. Margenot et al.· Proceedings of the 32nd ACM...· 0 citations
Tensor methods have played a central role in analyzing multi-dimensional data across a wide range of real-world applications. At the same time, these methods provide low-rank representations, which enable trustworthy and parsimonious machine learning systems. However, even though they hold great promise, tensor methods...
Dawon Ahn, Yong-chan Park, Taehyung Kwon et al.· Proceedings of the 32nd ACM...· 0 citations
This workshop aims to provide a forum for advancing tensor methods and applying them to various applications at the intersection of data mining and modern machine learning to foster an interactive environment for researchers to exchange ideas and build connections across communities.
Dawon Ahn, Yong-chan Park, Taehyung Kwon et al.· Proceedings of the 32nd ACM...· 0 citations
ProgNet, a graph learning framework for interpretable graph classification that treats explanatory structures as first-class, reusable components of the prediction mechanism, introduces a paradigm where reasoning is grounded in a shared vocabulary of reusable structural programs.
Minseok Jeon, Seunghyun Park, Jun-Gi Jang· Proceedings of the 32nd ACM...· 0 citations
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