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Bayesian Sparse Factor Models and DAGs Inference and Comparison.

, and . NIPS, page 736-744. Curran Associates, Inc., (2009)

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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)Counterfactual Representation Learning with Balancing Weights., , , , , , , and . CoRR, (2020)Weakly supervised cross-domain alignment with optimal transport., , , , , , , , and . CoRR, (2020)Translating ethical and quality principles for the effective, safe and fair development, deployment and use of artificial intelligence technologies in healthcare., , , , , , , , , and 12 other author(s). J. Am. Medical Informatics Assoc., 31 (3): 705-713 (February 2024)A Deep-Learning Algorithm for Thyroid Malignancy Prediction From Whole Slide Cytopathology Images., , , , , and . CoRR, (2019)Neural Insights for Digital Marketing Content Design., , , , , and . KDD, page 4320-4332. ACM, (2023)Enabling counterfactual survival analysis with balanced representations., , , , , and . CHIL, page 133-145. ACM, (2021)Communication-Efficient Stochastic Gradient MCMC for Neural Networks., , , , and . AAAI, page 4173-4180. AAAI Press, (2019)TAMC: A deep-learning approach to predict motif-centric transcriptional factor binding activity based on ATAC-seq profile., and . PLoS Comput. Biol., 18 (9): 1009921 (September 2022)