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Variational Auto-Encoders (VAEs) are capable of learning latent representations for high dimensional data.
Matrix tree theorems
Chaiken, S. and Kleitman, D · 1978
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Tree-reweighted belief propagation algorithms and approximate ML estimation via pseudo-moment matching
Wainwright, M · 2003
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A variational principle for graphical models
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A new class of upper bounds on the log partition function
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Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance
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Learning a distance metric from a network
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Auto-encoding variational Bayes
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Stochastic backpropagation and approximate inference in deep generative models
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Spectral networks and locally connected networks on graphs
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Composing graphical models with neural networks for structured representations and fast inference
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Improved variational inference with inverse autoregressive flow
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Variational graph auto-encoders
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Learning convolutional neural networks for graphs
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Inductive representation learning on large graphs
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Initialization and coordinate optimization for multi-way matching
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Variational message passing with structured inference networks
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Comparing interpretable inference models for videos of physical motion
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Structvae: Tree-structured latent variable models for semi-supervised semantic parsing
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