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Deep Generative Model with Beta Bernoulli Process for Modeling and Learning Confounding Factors., , , , , and . CoRR, (2018)Enhancing Mixup-based Semi-Supervised Learning with Explicit Lipschitz Regularization., , and . ICDM, page 1046-1051. IEEE, (2020)Learning Object-Centric Dynamic Modes from Video and Emerging Properties., , , , , and . L4DC, volume 211 of Proceedings of Machine Learning Research, page 745-769. PMLR, (2023)Semi-supervised Learning by Disentangling and Self-ensembling over Stochastic Latent Space., , , and . MICCAI (6), volume 11769 of Lecture Notes in Computer Science, page 766-774. Springer, (2019)Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences., , , , , and . IPMI, volume 11492 of Lecture Notes in Computer Science, page 153-166. Springer, (2019)Noninvasive Epicardial and Endocardial Electrocardiographic Imaging of Scar-Related Ventricular Tachycardia., , , , and . CinC, www.cinc.org, (2016)NVMSurvey: Recent Advances and Comparative Analysis of Emerging Non-Volatile Memories (eNVMs)., , and . IISWC, page 229-231. IEEE, (2023)High-Dimensional Bayesian Optimization of Personalized Cardiac Model Parameters via an Embedded Generative Model., , , , and . MICCAI (2), volume 11071 of Lecture Notes in Computer Science, page 499-507. Springer, (2018)Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential., , , , , and . MICCAI (2), volume 11071 of Lecture Notes in Computer Science, page 508-516. Springer, (2018)Improving Generalization of Sequence Encoder-Decoder Networks for Inverse Imaging of Cardiac Transmembrane Potential., , , , and . CoRR, (2018)