论文标题
大学习
Big Learning
论文作者
论文摘要
大/基础模型的最新进展揭示了一条有希望的深度学习途径，路线图从大数据稳步转移到大型模型，再到（新引入的）大型学习。具体而言，大型学习通过同时对潜在不同领域的许多/全部/有条件/条件/边际数据分布进行建模，并通过一个通用的基础模型对许多/全部/所有连接/条件/边际数据分布进行建模，从而详尽利用了其大规模完整/不完整培训数据中固有的信息。 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 范例。进行多种实验以验证提出的大型学习的有效性。
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.
