Document Type
Article
Publication Title
arXiv
Abstract
Machine Learning (ML) is about computational methods that enable machines to learn concepts from experiences. In handling a wide variety of experiences ranging from data instances, knowledge, constraints, to rewards, adversaries, and lifelong interplay in an ever-growing spectrum of tasks, contemporary ML/AI research has resulted in a multitude of learning paradigms and methodologies. Despite the continual progresses on all different fronts, the disparate narrowly-focused methods also make standardized, composable, and reusable development of learning solutions difficult, and make it costly if possible to build AI agents that panoramically learn from all types of experiences. This paper presents a standardized ML formalism, in particular a standard equation of the learning objective, that offers a unifying understanding of diverse ML algorithms, making them special cases due to different choices of modeling components. The framework also provides guidance for mechanic design of new ML solutions, and serves as a promising vehicle towards panoramic learning with all experiences. © 2021, CC BY.
DOI
10.48550/arXiv.2108.07783
Publication Date
8-17-2021
Keywords
Machine Learning (cs.LG)
Recommended Citation
Z. Hu and E. Xing, "Panoramic learning with a standardized machine learning formalism", arXiv, Aug. 2021, doi: 10.48550/arXiv.2108.07783
Comments
Preprint: arXiv;
Archived with thanks to arXiv;
Preprint License: CC by 4.0;
Uploaded 20 May 2022