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Infinite Dropout for training Bayesian models from data streams., , , and . IEEE BigData, page 125-134. IEEE, (2019)An interpretable method for text summarization based on simplicial non-negative matrix factorization., , and . SoICT, page 57-64. ACM, (2014)Efficient label propagation for classification on information networks., , and . SoICT, page 41-46. ACM, (2012)Continual variational dropout: a view of auxiliary local variables in continual learning., , , , and . Mach. Learn., 113 (1): 281-323 (January 2024)A Spectral Viewpoint on Continual Relation Extraction., , , , and . EMNLP (Findings), page 9621-9629. Association for Computational Linguistics, (2023)Enriching User Representation in Neural Matrix Factorization., , , and . RIVF, page 1-6. IEEE, (2019)Enabling Hierarchical Dirichlet Processes to Work Better for Short Texts at Large Scale., , , , and . PAKDD (2), volume 9652 of Lecture Notes in Computer Science, page 431-442. Springer, (2016)Adaptive infinite dropout for noisy and sparse data streams., , , , and . Mach. Learn., 111 (8): 3025-3060 (2022)Boosting prior knowledge in streaming variational Bayes., , , , and . Neurocomputing, (2021)Reducing Catastrophic Forgetting in Neural Networks via Gaussian Mixture Approximation., , , , and . PAKDD (1), volume 13280 of Lecture Notes in Computer Science, page 106-117. Springer, (2022)