Search Results for author:
Found 3 papers, 1 papers with code
Mixture Representation Learning with Coupled Autoencoding Agents
Jointly identifying a mixture of discrete and continuous factors of variability can help unravel complex phenomena.
Mixture Representation Learning with Coupled Autoencoders
Jointly identifying a mixture of discrete and continuous factors of variability without supervision is a key problem in unraveling complex phenomena.
A coupled autoencoder approach for multi-modal analysis of cell types
Recent developments in high throughput profiling of individual neurons have spurred data driven exploration of the idea that there exist natural groupings of neurons referred to as cell types.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
