Panoramic Learning with A Standardized Machine Learning Formalism
- URL: http://arxiv.org/abs/2108.07783v1
- Date: Tue, 17 Aug 2021 17:44:38 GMT
- Title: Panoramic Learning with A Standardized Machine Learning Formalism
- Authors: Zhiting Hu, Eric P. Xing
- Abstract summary: This paper presents a standardized equation of the learning objective, that offers a unifying understanding of diverse ML algorithms.
It also provides guidance for mechanic design of new ML solutions, and serves as a promising vehicle towards panoramic learning with all experiences.
- Score: 116.34627789412102
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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