A brief note on understanding neural networks as Gaussian processes
As a generalization of the work in [Lee et al., 2017], this note briefly discusses when the prior of a neural network output follows a Gaussian process, and how a neural-network-induced Gaussian process is formulated. The posterior mean functions of such a Gaussian process regression lie in the reproducing kernel Hilbert space defined by the neural-network-induced kernel. In the case of two-layer neural networks, the induced Gaussian processes provide an interpretation of the reproducing kernel Hilbert spaces whose union forms a Barron space.
Code (0)
등록된 구현이 없습니다.
Tasks
Gaussian ProcessesregressionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
The Use of Gaussian Processes in System Identification
Gaussian processes are used in machine learning to learn input-output mappings from observed data. Gaussian process regression is based on imposing a Gaussian process prior on the unknown regressor function and statistic…
FormGaussian ProcessesState Space ModelsTime Series+2Introduction to intelligent computing unit 1
This brief note highlights some basic concepts required toward understanding the evolution of machine learning and deep learning models. The note starts with an overview of artificial intelligence and its relationship to…
BIG-bench Machine LearningDeep LearningBriefly Noted: Briefly Noted
Deep Gaussian Processes: A Survey
Gaussian processes are one of the dominant approaches in Bayesian learning. Although the approach has been applied to numerous problems with great success, it has a few fundamental limitations. Multiple methods in litera…
Gaussian ProcessesSurveyNotes About a More Aware Dependency Parser
In this paper I explain the reasons that led me to research and conceive a novel technology for dependency parsing, mixing together the strengths of data-driven transition-based and constraint-based approaches. In partic…
Dependency Parsing