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In 1996 AAAI Spring Symposium on Machine Learning in Information Access, pages 18--25, 1996."},{"key":"key-10.1145\/3041021.3055166-8","doi-asserted-by":"crossref","unstructured":"D. Di Castro, Z. Karnin, L. Lewin-Eytan, and Y. Maarek. You've got mail, and here is what you could do with it!: Analyzing and predicting actions on email messages. In 9th ACM International Conference on Web Search and Data Mining, pages 307--316, 2016.","DOI":"10.1145\/2835776.2835811"},{"key":"key-10.1145\/3041021.3055166-9","doi-asserted-by":"crossref","unstructured":"I. Gamzu, Z. Karnin, Y. Maarek, and D. Wajc. You will get mail! Predicting the arrival of future email. In 24th International Conference on World Wide Web, pages 1327--1332, 2015.","DOI":"10.1145\/2740908.2741694"},{"key":"key-10.1145\/3041021.3055166-10","unstructured":"I. Goodfellow, Y. Bengio, and A. Courville. Deep learning. Book in preparation for MIT Press, 2016."},{"key":"key-10.1145\/3041021.3055166-11","doi-asserted-by":"crossref","unstructured":"A. Graves. Neural networks. In Supervised Sequence Labelling with Recurrent Neural Networks, pages 15--35. Springer, 2012.","DOI":"10.1007\/978-3-642-24797-2_3"},{"key":"key-10.1145\/3041021.3055166-12","doi-asserted-by":"crossref","unstructured":"M. Grbovic, G. Halawi, Z. Karnin, and Y. Maarek. How many folders do you really need?: Classifying email into a handful of categories. In 23rd ACM International Conference on Conference on Information and Knowledge Management, pages 869--878, 2014.","DOI":"10.1145\/2661829.2662018"},{"key":"key-10.1145\/3041021.3055166-13","doi-asserted-by":"crossref","unstructured":"R. Gupta, G. Liang, H.-P. Tseng, R. K. Holur Vijay, X. Chen, and R. Rosales. Email volume optimization at LinkedIn. In 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 97--106, 2016.","DOI":"10.1145\/2939672.2939692"},{"key":"key-10.1145\/3041021.3055166-14","doi-asserted-by":"crossref","unstructured":"S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural computation, 9(8):1735--1780, 1997.","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"key-10.1145\/3041021.3055166-15","doi-asserted-by":"crossref","unstructured":"J.-Y. Jiang, Y.-Y. Ke, P.-Y. Chien, and P.-J. Cheng. Learning user reformulation behavior for query auto-completion. In 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pages 445--454, 2014.","DOI":"10.1145\/2600428.2609614"},{"key":"key-10.1145\/3041021.3055166-16","doi-asserted-by":"crossref","unstructured":"A. Kannan, K. Kurach, S. Ravi, T. Kaufmann, A. Tomkins, B. Miklos, G. Corrado, L. Lukács, M. Ganea, P. Young, and V. Ramavajjala. Smart reply: Automated response suggestion for email. In 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 495--503, 2016.","DOI":"10.1145\/2939672.2939801"},{"key":"key-10.1145\/3041021.3055166-17","unstructured":"D. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv preprint:1412.6980, 2014."},{"key":"key-10.1145\/3041021.3055166-18","unstructured":"S. Kiritchenko and S. Matwin. Email classification with co-training. In 2001 Conference of the Center for Advanced Studies on Collaborative Research, page 8, 2001."},{"key":"key-10.1145\/3041021.3055166-19","doi-asserted-by":"crossref","unstructured":"B. Klimt and Y. Yang. The Enron corpus: A new dataset for email classification research. In 15th European Conference on Machine Learning, pages 217--226, 2004.","DOI":"10.1007\/978-3-540-30115-8_22"},{"key":"key-10.1145\/3041021.3055166-20","doi-asserted-by":"crossref","unstructured":"Y. Koren, E. Liberty, Y. Maarek, and R. Sandler. Automatically tagging email by leveraging other users' folders. In 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 913--921, 2011.","DOI":"10.1145\/2020408.2020560"},{"key":"key-10.1145\/3041021.3055166-21","doi-asserted-by":"crossref","unstructured":"Y. Maarek. Is mail the next frontier in search and data mining? In 9th ACM International Conference on Web Search and Data Mining, pages 203--203, 2016.","DOI":"10.1145\/2835776.2835847"},{"key":"key-10.1145\/3041021.3055166-22","doi-asserted-by":"crossref","unstructured":"V. Pham, T. Bluche, C. Kermorvant, and J. Louradour. Dropout improves recurrent neural networks for handwriting recognition. In 14th International Conference on Frontiers in Handwriting Recognition, pages 285--290, 2014.","DOI":"10.1109\/ICFHR.2014.55"},{"key":"key-10.1145\/3041021.3055166-23","doi-asserted-by":"crossref","unstructured":"L. Prechelt. Automatic early stopping using cross validation: quantifying the criteria. Neural Networks, 11(4):761--767, 1998.","DOI":"10.1016\/S0893-6080(98)00010-0"},{"key":"key-10.1145\/3041021.3055166-24","unstructured":"J. Provost. Naïve-bayes vs. rule-learning in classification of email. University of Texas at Austin, 1999."},{"key":"key-10.1145\/3041021.3055166-25","doi-asserted-by":"crossref","unstructured":"W. J. Stewart. Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling. Princeton University Press, Princeton, NJ, USA, 2009.","DOI":"10.2307\/j.ctvcm4gtc"},{"key":"key-10.1145\/3041021.3055166-26","doi-asserted-by":"crossref","unstructured":"B. Wang, M. Ester, J. Bu, Y. Zhu, Z. Guan, and D. Cai. Which to view: Personalized prioritization for broadcast emails. In 25th International Conference on World Wide Web, pages 1181--1190, 2016.","DOI":"10.1145\/2872427.2883049"},{"key":"key-10.1145\/3041021.3055166-27","doi-asserted-by":"crossref","unstructured":"B. Wang, M. Ester, Y. Liao, J. Bu, Y. Zhu, Z. Guan, and D. Cai. 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