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Data Sheet 1_Flow-based parameterization for DAG and feature discovery in scientific multimodal data.pdf
Published 2024“…To discover features and their conditional independence structure, we develop pimaDAG, a variational autoencoder framework that learns features from multimodal datasets, possibly with known physics constraints, and a BN describing the feature distribution. Our algorithm introduces a new DAG parameterization, which we use to learn a BN simultaneously with a latent space of a variational autoencoder in an end-to-end differentiable framework via a single, tractable evidence lower bound loss function. …”
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Convergence curve of others functions compared with the PSO family algorithms.
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Convergence curve of unimodal functions compared with the PSO family algorithms.
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Convergence curve of unimodal functions compared with the meta-heuristic algorithms.
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Convergence curve of others functions compared with the meta-heuristic algorithms.
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Convergence curve of multimodal functions compared with the meta-heuristic algorithms.
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Convergence curve of multimodal functions compared with the PSO family algorithms.
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