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Found 3 papers, 1 papers with code
A primer on synthetic health data
Recent advances in deep generative models have greatly expanded the potential to create realistic synthetic health datasets.
The Deep Generative Decoder: MAP estimation of representations improves modeling of single-cell RNA data
Learning low-dimensional representations of single-cell transcriptomics has become instrumental to its downstream analysis.
A manifold learning perspective on representation learning: Learning decoder and representations without an encoder
Our approach of training the decoder alone facilitates representation learning even on small data sets and can lead to improved training of autoencoders.
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