May 22, 2023 · 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 …

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

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 …

May 22, 2023 · Title: RDA-INR: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations Authors: Sven Dummer , Nicola Strisciuglio , Christoph Brune (Submitted on …

To achieve this goal, we introduce a novel model called Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations (RDA-INR). Our model is designed to deal with the …

Nov 29, 2022 · 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）回 …

We showcase that the Riemannian geometry aspect improves latent modeling and is required for a proper mean-variance analysis. Furthermore, to showcase the benefit of resolution …

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