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Machine Learning with Marginalized Corruption

Machine Learning with Marginalized Corruption. ML classification improves when corrupted samples are added to the training set We discovered a way to learn from infinite training data by marginalizing out the corruption [1]

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Machine Learning with Marginalized Corruption

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  1. Machine Learning with Marginalized Corruption • ML classification improves when corrupted samples are added to the training set • We discovered a way to learn from infinite training data by marginalizing out the corruption [1] • This method can be applied to neural networks [2] and image applications [3] [1] L. van der Maaten, M. Chen, S. Tyree, and K. Q. Weinberger. Learning with Marginalized Corrupted Features. Proceedings of 30th International Conference on Machine Learning (ICML), Atlanta, GA, pages 410-418, 2013. [2] MinminChen, Zhixiang (Eddie) Xu, K. Q. Weinberger, F. Sha. Marginalized Stacked DenoisingAutoencoders for Domain Adaptation. Proceedings of 29th International Conference on Machine Learning (ICML), Edinburgh Scotland, Omnipress, pages 767-774, 2012. [3] M. Chen, Alice Zheng, K.Q. Weinberger. Fast Image Tagging. Proceedings of 30th International Conference on Machine Learning (ICML), Atlanta, GA, pages 1274-1282, 2013.

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