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Found 6 papers, 2 papers with code
Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder
Motivation: Single cell RNA sequencing (scRNA-seq) data makes studying the development of cells possible at unparalleled resolution.
Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Data
Clustering high-dimensional data, such as images or biological measurements, is a long-standingproblem and has been studied extensively.
Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations
MoE-Sim-VAE is based on a Variational Autoencoder (VAE), where the decoder consists of a Mixture-of-Experts (MoE) architecture.
Coupling weak and strong supervision for classification of prostate cancer histopathology images
Automated grading of prostate cancer histopathology images is a challenging task, with one key challenge being the scarcity of annotations down to the level of regions of interest (strong labels), as typically the prostate cancer Gleason score is known only for entire tissue slides (weak labels).
A Fused Elastic Net Logistic Regression Model for Multi-Task Binary Classification
Multi-task learning has shown to significantly enhance the performance of multiple related learning tasks in a variety of situations.
Markov Network Structure Learning via Ensemble-of-Forests Models
Real world systems typically feature a variety of different dependency types and topologies that complicate model selection for probabilistic graphical models.
