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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)

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Learning Deep Sigmoid Belief Networks with Data Augmentation., , , and . AISTATS, volume 38 of JMLR Workshop and Conference Proceedings, JMLR.org, (2015)Inference of gene networks associated with the host response to infectious disease., , , , and . Big Data over Networks, Cambridge University Press, (2016)Communication-Efficient Stochastic Gradient MCMC for Neural Networks., , , , and . AAAI, page 4173-4180. AAAI Press, (2019)Towards Unifying Hamiltonian Monte Carlo and Slice Sampling., , , , , and . NIPS, page 1741-1749. (2016)Deep Temporal Sigmoid Belief Networks for Sequence Modeling., , , , and . NIPS, page 2467-2475. (2015)Wasserstein Contrastive Representation Distillation., , , , , and . CVPR, page 16296-16305. Computer Vision Foundation / IEEE, (2021)ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching., , , , , , and . NIPS, page 5495-5503. (2017)Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment., , , and . EMNLP, page 1829-1838. Association for Computational Linguistics, (2018)A Deep-Learning Algorithm for Thyroid Malignancy Prediction From Whole Slide Cytopathology Images., , , , , and . CoRR, (2019)Enabling counterfactual survival analysis with balanced representations., , , , , and . CHIL, page 133-145. ACM, (2021)