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Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification.

, , , , , and . CVPR, page 5666-5675. IEEE Computer Society, (2016)

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A Deep Generative Deconvolutional Image Model., , , , and . AISTATS, volume 51 of JMLR Workshop and Conference Proceedings, page 741-750. JMLR.org, (2016)Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling., , , , , and . ACL (1), page 321-331. Association for Computational Linguistics, (2017)Towards Understanding Adversarial Learning for Joint Distribution Matching., , , , , , and . CoRR, (2017)Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification., , , , , and . CVPR, page 5666-5675. IEEE Computer Society, (2016)JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets., , , , , , , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 4148-4157. PMLR, (2018)Semantic Compositional Networks for Visual Captioning., , , , , , , and . CVPR, page 1141-1150. IEEE Computer Society, (2017)Adaptive Feature Abstraction for Translating Video to Text., , , and . AAAI, page 7284-7291. AAAI Press, (2018)Triangle Generative Adversarial Networks., , , , , , , and . NIPS, page 5247-5256. (2017)lambda-Net: Reconstruct Hyperspectral Images From a Snapshot Measurement., , , and . ICCV, page 4058-4068. IEEE, (2019)Deep Generative Models for Image Representation Learning.. Duke University, Durham, NC, USA, (2018)base-search.net (ftdukeunivdsp:oai:localhost:10161/16806).