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Modeling Progression Free Survival in Breast Cancer with Tensorized Recurrent Neural Networks and Accelerated Failure Time Models.

, , and . MLHC, volume 68 of Proceedings of Machine Learning Research, page 164-176. PMLR, (2017)

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Mixture Approximations to Bayesian Networks., , and . UAI, page 639-646. Morgan Kaufmann, (1999)Some Solutions to the Missing Feature Problem in Vision., and . NIPS, page 393-400. Morgan Kaufmann, (1992)A Solution for Missing Data in Recurrent Neural Networks with an Application to Blood Glucose Prediction., and . NIPS, page 971-977. The MIT Press, (1997)Transductive and Inductive Methods for Approximate Gaussian Process Regression., and . NIPS, page 953-960. MIT Press, (2002)Reducing the Rank in Relational Factorization Models by Including Observable Patterns., , and . NIPS, page 1179-1187. (2014)Tensor Computing for Internet of Things (Dagstuhl Perspectives Workshop 16152)., , , , and . Dagstuhl Reports, 6 (4): 57-79 (2016)Predicting the co-evolution of event and Knowledge Graphs., , , , and . FUSION, page 98-105. IEEE, (2016)A Bayesian Committee Machine.. Neural Computation, 12 (11): 2719-2741 (2000)Knowledge Acquisition and Automated Generation of Bayesian Networks for a Medical Dialogue and Advisory System., , , , , , and . AIME, volume 2101 of Lecture Notes in Computer Science, page 199-202. Springer, (2001)Efficient Dialog Policy Learning via Positive Memory Retention., and . SLT, page 823-830. IEEE, (2018)