Unsupervised learning

Addressing Model Collapse in Gaussian Process Latent Variable Models through Projection Variance Learning and Flexible Kernel Integration

The core message of this paper is to address the issue of model collapse in Gaussian Process Latent Variable Models (GPLVMs) by: 1) theoretically examining the impact of the projection variance on model collapse, and 2) integrating a flexible spectral mixture kernel with a differentiable random Fourier feature approximation to enhance kernel flexibility and enable efficient and scalable learning.

다중 그래프의 신뢰성 있는 정보 추출을 통한 비지도 그래프 구조 학습

무감독 단어 발견: 클러스터링 vs. 동적 프로그래밍을 이용한 경계 탐지

Addressing Model Collapse in Gaussian Process Latent Variable Models through Projection Variance Learning and Flexible Kernel Integration