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Spatially resolved transcriptomics (SRT) provides an unprecedented opportunity to investigate the complex and heterogeneous tissue organization. To address these issues, we propose a VAD-disentangled Variational AutoEncoder (VAD-VAE), which first introduces a target utterance reconstruction task.
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    The difference of both is that i) auto encoders do not encourage sparsity in their general form ii) an autoencoder uses a model for finding the codes, while sparse coding does so by means of optimisation.

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    However, current inference methods for these Bayesian models adopt restricted update rules for the posterior.

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  • Jun 5, 2018 · class=" fc-falcon">Techopedia Explains Sparse Autoencoder.
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  • In the real-world applications, the medical data are subject to some noise (such as missing values and outliers).
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