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Simplifying Graph Convolutional Networks

, , , , , and . Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, page 6861--6871. PMLR, (09--15 Jun 2019)

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Simplifying Graph Convolutional Networks, , , , , and . Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, page 6861--6871. PMLR, (09--15 Jun 2019)From word embeddings to document distances, , , and . International conference on machine learning, page 957--966. (2015)Convex Optimizations for Distance Metric Learning and Pattern Classification Applications Corner., , and . IEEE Signal Process. Mag., 27 (3): 146-158 (2010)On Calibration of Modern Neural Networks, , , and . roceedings of the 34th International Conference on Machine Learning, 70, page 1321-1330. PMLR, (2017)Anytime Representation Learning., , , and . ICML (3), volume 28 of JMLR Workshop and Conference Proceedings, page 1076-1084. JMLR.org, (2013)Learning with Marginalized Corrupted Features., , , and . ICML (1), volume 28 of JMLR Workshop and Conference Proceedings, page 410-418. JMLR.org, (2013)Bayesian Optimization with Inequality Constraints., , , , and . ICML, volume 32 of JMLR Workshop and Conference Proceedings, page 937-945. JMLR.org, (2014)Spectral Methods for Dimensionality Reduction., , , , and . Semi-Supervised Learning, The MIT Press, (2006)Transductive Minimax Probability Machine., , , and . ECML/PKDD (1), volume 8724 of Lecture Notes in Computer Science, page 579-594. Springer, (2014)Revisiting Meta-Learning as Supervised Learning., , , , and . CoRR, (2020)