Big Learning

• URL: http://arxiv.org/abs/2207.03899v4
• Date: Sun, 21 May 2023 04:05:46 GMT
• Title: Big Learning
• Authors: Yulai Cong, Miaoyun Zhao
• Abstract summary: Big learning exploits the information inherent in its large-scale complete/incomplete training data. Big learning is capable of delivering all joint/conditional/marginal data capabilities with one universal model. Diverse experiments are carried out to validate the effectiveness of the presented big learning.
• Score: 15.733273085085763
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in big/foundation models reveal a promising path for deep learning, where the roadmap steadily moves from big data to big models to (the newly-introduced) big learning. Specifically, the big learning exhaustively exploits the information inherent in its large-scale complete/incomplete training data, by simultaneously modeling many/all joint/conditional/marginal data distributions across potentially diverse domains, with one universal foundation model. We reveal that big learning ($i$) underlies most existing foundation models, ($ii$) is equipped with extraordinary flexibilities for complete/incomplete training data and trustworthy data tasks, ($iii$) is capable of delivering all joint/conditional/marginal data capabilities with one universal model, and ($iv$) unifies conventional machine learning paradigms and enables their flexible cooperations, manifested as a universal learning paradigm. Diverse experiments are carried out to validate the effectiveness of the presented big learning.

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