Model Catalog

Graph neural network with MoCo-based contrastive learning for single-cell clustering and embedding (encoder-only)

MoCo-based contrastive learning framework for learning robust cell representations (encoder-only)

Deep generative VAE model for scRNA-seq with probabilistic inference and batch correction

Graph autoencoder that models cell-cell relationships through cell graphs for imputation and clustering

Scalable VAE for online integration of single-cell data by projecting into batch-invariant space

Neural network VAE method for cell searching and annotation via unbiased cell embedding

Deep autoencoder with Dirichlet Process Mixture Model for adaptive cell clustering

Autoencoder with Zero-Inflated Negative Binomial reconstruction and DEC-style clustering

Hyperbolic VAE with ZINB reconstruction and t-SNE repulsion for hierarchical cell visualization

ResNet autoencoder with mutual information clustering for single-cell analysis

Conditional diffusion model for high-quality single-cell data generation with cell-type control

VAE with structured latent space for interpretable single-cell modeling

Deep generative VAE model for single-cell chromatin accessibility peak analysis

Deep generative VAE model for quantitative scATAC-seq fragment counts using Poisson likelihood

Trajectory inference VAE for learning cell developmental paths in single-cell data

VAE with Product of Experts Gaussian mixture prior for clustering in Euclidean space

GMVAE in hyperbolic Poincaré space for hierarchical cell type relationships

GMVAE using Hyperbolic-Wrapped distributions for hierarchical clustering on Lorentz hyperboloid

GMVAE with learnable curvature PGM for adaptive geometric structure

VAE with weighted KL divergence for learning disentangled factors

VAE with mutual information maximization for disentangled and informative representations

VAE that explicitly decomposes and minimizes total correlation for disentanglement

VAE that learns a factorial prior to encourage disentanglement