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Subset-Conditioned Generation Using Variational Autoencoder With A Learnable Tensor-Train Induced Prior

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Doubly Semi-Implicit Variational Inference, , , and . Proceedings of Machine Learning Research, volume 89 of Proceedings of Machine Learning Research, page 2593--2602. PMLR, (16--18 Apr 2019)Variance Networks: When Expectation Does Not Meet Your Expectations, , , and . (2018)cite arxiv:1803.03764.Variational Autoencoders for Studying the Manifold of Precoding Matrices with High Spectral Efficiency., , and . CoRR, (2021)Variance Networks: When Expectation Does Not Meet Your Expectations, , , and . International Conference on Learning Representations, (2019)Subset-Conditioned Generation Using Variational Autoencoder With A Learnable Tensor-Train Induced Prior, , , and . (2018)Bayesian Incremental Learning for Deep Neural Networks., , , , , and . ICLR (Workshop), OpenReview.net, (2018)Metropolis-Hastings view on variational inference and adversarial training., , and . CoRR, (2018)Few-shot Generative Modelling with Generative Matching Networks., and . AISTATS, volume 84 of Proceedings of Machine Learning Research, page 670-678. PMLR, (2018)ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks., , and . ACML, volume 95 of Proceedings of Machine Learning Research, page 422-437. PMLR, (2018)A new approach for sparse Bayesian channel estimation in SCMA uplink systems., , , and . WCSP, page 1-5. IEEE, (2016)