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Deconvolutional Latent-Variable Model for Text Sequence Matching.

, , , , and . AAAI, page 5438-5445. AAAI Press, (2018)

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Deconvolutional Latent-Variable Model for Text Sequence Matching., , , , and . AAAI, page 5438-5445. AAAI Press, (2018)Unsupervised Learning with Truncated Gaussian Graphical Models., , , , and . CoRR, (2016)Convergence Analysis of the Variance in Gaussian Belief Propagation., and . IEEE Trans. Signal Processing, 62 (19): 5119-5131 (2014)Distributed Estimation of Variance in Gaussian Graphical Model via Belief Propagation: Accuracy Analysis and Improvement., and . IEEE Trans. Signal Processing, 63 (23): 6258-6271 (2015)Document Hashing with Mixture-Prior Generative Models., , , and . EMNLP/IJCNLP (1), page 5225-5234. Association for Computational Linguistics, (2019)Symmetric Variational Autoencoder and Connections to Adversarial Learning., , , , , , , and . AISTATS, volume 84 of Proceedings of Machine Learning Research, page 661-669. PMLR, (2018)NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing., , , , , , and . ACL (1), page 2041-2050. Association for Computational Linguistics, (2018)Nonlinear Statistical Learning with Truncated Gaussian Graphical Models., , , and . ICML, volume 48 of JMLR Workshop and Conference Proceedings, page 1948-1957. JMLR.org, (2016)A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks., , and . NIPS, page 4486-4495. (2017)Complexity reduction of signal detection by exploiting correlation characteristics of spreading sequences., , , and . Int. J. Communication Systems, 22 (11): 1427-1443 (2009)