Variational graph encoders effectively combine graph convolutional networks with variational autoencoders, and have been widely employed for biomedical graph-structured data. Lam and colleagues developed a framework based on the variational graph encoder
This model combines the probabilistic interpretation of CCA with an autoencoder architecture based on graph convolutional neural network layers. Experiments with the proposed method are conducted on classification, clustering, and recommendation tasks on real datasets. The algorithm is competitive with state...
Data-driven medical care delivery must always respect patient privacy—a requirement that is not easily met. This issue has impeded improvements to healthcare software and has delayed the long-predicted prevalence of artificial intelligence in healthcare
Initially, we use a variational graph autoencoder (VGAE) to compress the augmented gene expression data into a low-dimensional latent representation Z. A clustering layer, denoted as {μj}j=1J, is subsequently introduced within the encoder's latent space, where J represents the total number ...
Fig. 3: Performance of variational autoencoder models. Comparison of TopoGNN, GNN, and Topo in terms of polymer graph reconstruction, \(\langle {R}_{{{\rm{g}}}^{2}\rangle\) regression, and topology classification. BACC represents balanced accuracy, R2 is the coefficient of determination...
Cite this paper Li, X., Lyu, X., Zhang, H., Hu, K., Tang, Z. (2019). Regularizing Variational Autoencoders for Molecular Graph Generation. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol ...
{Multiresolution equivariant graph variational autoencoder}, journal = {Machine Learning: Science and Technology}, abstract = {In this paper, we propose Multiresolution Equivariant Graph Variational Autoencoders (MGVAE), the first hierarchical generative model to learn and generate graphs in a multi...
Cite As Kenta (2025). Conditional VAE (Variational Auto Encoder) 条件付きVAE (https://www.mathworks.com/matlabcentral/fileexchange/74974-conditional-vae-variational-auto-encoder-vae), MATLAB Central File Exchange. Retrieved May 16, 2025. Requires MATLAB Deep Learning Too...
Variational autoencoders (VAEs), introduced by Kingma and Welling in 2013,21are generative models based on layered neural networks. Given a set of i.i.d. data pointsX = {x(i)}, where\(x^{(i)} \in {\Bbb R}^n\), generated from some distributionpθ(x(i)|z) over Gaussian...
To address these issues, we developed an open-source machine learning model, Joint Variational Autoencoders for multimodal Imputation and Embedding (JAMIE). JAMIE takes single-cell multimodal data that can have partially matched samples across modalities. Variational autoencoders learn the latent ...