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Replication Data for: Implicit Gaussian process representation of vector fields over arbitrary latent manifolds

NIAID Data Ecosystem2026-05-02 收录

流形学习

高斯过程建模

数据链接：

https://doi.org/10.7910/DVN/PODAZ8 数据链接链接失效反馈

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资源简介：

Contains example data for RVGP paper published at ICLR 2024

应用场景：

创建时间：

2024-10-28

数据集成

流形学习

This article focuses on data integration problems where the predictor variables for some response variable partition into known subsets. This type of data is often referred to as multi-view data, and

DataCite Commons2020-08-29 更新140

Supervised Manifold Learning for Functional Data

功能数据分析

流形学习

Classification is a core topic in functional data analysis. A large number of functional classifiers have been proposed in the literature, most of which are based on functional principal component ana

Taylor & Francis Group2025-10-20 更新70

Maximum expected entropy transformed Latin hypercube designs

实验设计优化

高斯过程建模

Existing projection designs (e.g. maximum projection designs) attempt to achieve good space-filling properties in all projections. However, when using a Gaussian process (GP), model-based design crite

Taylor & Francis Group2021-08-27 更新60

MOESM3 of ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks

基因网络

流形学习

Additional file 3 Tutorial, including: ManiNetCluster Tutorial.

DataCite Commons2020-08-26 更新100

GPerturb: Gaussian process modelling of single-cell perturbation data

单细胞扰动分析

高斯过程建模

Pre-trained models and associated processed datasets for all analyses in the paper

Figshare2024-08-04 更新60