2023年5月22日 · To highlight the benefit of resolution independence for LDDMM-based data variability modeling, we show that our approach outperforms current neural network-based …

To highlight the benefit of resolution independence for LDDMM-based data variability modeling, we show that our approach outperforms current neural network–based LDDMM latent code …

2023年5月22日 · To highlight the benefit of resolution independence for LDDMM-based data variability modeling, we show that our approach outperforms current neural network-based …

SUPPLEMENTARY MATERIALS: RDA-INR: Riemannian Di eomorphic Autoencoding via Implicit Neural Representations Sven Dummery, Nicola Strisciuglioz, and Christoph Bruney SM1. …

We introduce a novel model called Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations (RDA-INR) that deals with the aforementioned issues. Figure 1 shows how …

We investigate how the Riemannian geometry improves latent modeling and is required for a proper mean-variance analysis. To highlight the benefit of resolution independence for …

2022年11月29日 · 从概念上讲，冗余分析（redundancy analysis, RDA）是响应变量矩阵与解释变量矩阵之间多元多重线性回归的拟合值矩阵的PCA分析，也是多响应变量（multi-response） …

2018年10月6日 · 在生态环境领域中，我们研究的对象可被分为两种：“解释变量”与“响应变量”，常用的分析方法主要有约束排序和非约束排序，常用软件为CANOCO和R，前者容易上手，操作 …

2021年12月11日 · RDA files can be opened with the free RStudio tool for Windows, macOS, and Linux systems in addition to R. Rdata files are those having a .rda extension. To save certain …

2023年5月22日 · To highlight the benefit of resolution independence for LDDMM-based data variability modeling, we show that our approach outperforms current neural network-based …