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The document introduces Deep Disentangled Interleaving Variational Encoding (DeepDIVE), a model designed to address the challenges of conflicting objectives in multi-task learning by disentangling input data into marginal and conditional probability distributions within a variational autoencoder framework. The authors theoretically derive a loss function for this model and demonstrate its effectiveness through experiments on public datasets, showing improved forecast accuracy compared to traditional VAEs an

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eycheu

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EN