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Habrard and M. Sebban. 2013. A survey on metric learning for feature vectors and structured data. arXiv:1306.6709 (2013).  A. Bellet A. Habrard and M. Sebban. 2013. A survey on metric learning for feature vectors and structured data. arXiv:1306.6709 (2013)."},{"key":"e_1_3_2_2_5_1","unstructured":"M. Bober-Irizar S. Husain E. J. Ong and M. Bober. 2017. Cultivating DNN Diversity for Large Scale Video Labelling. arXiv:1707.04272 (2017).  M. Bober-Irizar S. Husain E. J. Ong and M. Bober. 2017. Cultivating DNN Diversity for Large Scale Video Labelling. arXiv:1707.04272 (2017)."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"crossref","unstructured":"J. Bromley I. Guyon Y. LeCun E. S\u00e4ckinger and R. Shah. 1994. Signature verification using a \"siamese\" time delay neural network. Advances in Neural Information Processing Systems (1994).   J. Bromley I. Guyon Y. LeCun E. S\u00e4ckinger and R. Shah. 1994. Signature verification using a \"siamese\" time delay neural network. Advances in Neural Information Processing Systems (1994).","DOI":"10.1142\/9789812797926_0003"},{"key":"e_1_3_2_2_7_1","unstructured":"S. Chen X. Wang Y. Tang X. Chen Z. Wu and Y. G. Jiang. 2017. Aggregating frame-level features for large-scale video classification. arXiv:1707.00803 (2017).  S. Chen X. Wang Y. Tang X. Chen Z. Wu and Y. G. Jiang. 2017. Aggregating frame-level features for large-scale video classification. arXiv:1707.00803 (2017)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.202"},{"key":"e_1_3_2_2_9_1","unstructured":"L. de Campos J. Fernandez-Luna J. Huete and M. Rueda-Morales. 2010. International Journal of Approximate Reasoning. International Journal of Approximate Reasoning (2010).  L. de Campos J. Fernandez-Luna J. Huete and M. Rueda-Morales. 2010. International Journal of Approximate Reasoning. International Journal of Approximate Reasoning (2010)."},{"key":"e_1_3_2_2_10_1","unstructured":"D. Duvenaud D. Maclaurin J. Aguilera-Iparraguirre R. G\u00f3mez-Bombarelli T. Hirzel A. Aspuru-Guzik and R. P. Adams. 2015. Convolutional Networks on Graphs for Learning Molecular Fingerprints Advances in Neural Information Processing Systems.   D. Duvenaud D. Maclaurin J. Aguilera-Iparraguirre R. G\u00f3mez-Bombarelli T. Hirzel A. Aspuru-Guzik and R. P. Adams. 2015. Convolutional Networks on Graphs for Learning Molecular Fingerprints Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2484028.2484176"},{"key":"e_1_3_2_2_12_1","unstructured":"B. Fernando and S. Gould. 2016. Learning end-to-end video classification with rank-pooling Proc. of the International Conference on Machine Learning.   B. Fernando and S. Gould. 2016. Learning end-to-end video classification with rank-pooling Proc. of the International Conference on Machine Learning."},{"key":"e_1_3_2_2_13_1","unstructured":"J. Friedman et al. 1994. Flexible metric nearest neighbor classification. Technical Report. Technical report Dept. of Statistics Stanford University.  J. Friedman et al. 1994. Flexible metric nearest neighbor classification. Technical Report. Technical report Dept. of Statistics Stanford University."},{"key":"e_1_3_2_2_14_1","unstructured":"R. Geirhos D. H. J. Janssen H. H. Sch\u00fctt J. Rauber M. Bethge and F. A. Wichmann. 2017. Comparing deep neural networks against humans: object recognition when the signal gets weaker. arXiv:170606969 (2017).  R. Geirhos D. H. J. Janssen H. H. Sch\u00fctt J. Rauber M. Bethge and F. A. Wichmann. 2017. 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Fine-grained visual categorization via multi-stage metric learning . Proc. of the IEEE Conference on Computer Vision and Pattern Recognition ( 2015 ). Q. Qian, R. Jin, S. Zhu, and Y. Lin. 2015. Fine-grained visual categorization via multi-stage metric learning. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (2015)."},{"key":"e_1_3_2_2_39_1","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition.","author":"Real E.","unstructured":"E. Real , J. Shlens , S. Mazzocchi , X. Pan , and V. Vanhoucke . 2017. Youtube-boundingboxes: A large high-precision human-annotated data set for object detection in video . In IEEE Conference on Computer Vision and Pattern Recognition. E. Real, J. Shlens, S. Mazzocchi, X. Pan, and V. Vanhoucke. 2017. Youtube-boundingboxes: A large high-precision human-annotated data set for object detection in video. 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Some like it hot - visual guidance for preference prediction Proc. of the IEEE Conference on Computer Vision and Pattern Recognition.","DOI":"10.1109\/CVPR.2016.599"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390267"},{"key":"e_1_3_2_2_43_1","volume-title":"Facenet: A unified embedding for face recognition and clustering Proc. of the IEEE Conference on Computer Vision and Pattern Recognition.","author":"Schroff F.","year":"2015","unstructured":"F. Schroff , D. Kalenichenko , and J. Philbin . 2015 . Facenet: A unified embedding for face recognition and clustering Proc. of the IEEE Conference on Computer Vision and Pattern Recognition. F. Schroff, D. Kalenichenko, and J. Philbin. 2015. Facenet: A unified embedding for face recognition and clustering Proc. of the IEEE Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1981.1056403"},{"key":"e_1_3_2_2_45_1","unstructured":"M. Skalic M. Pekalski and X. Pan. 2017. Deep Learning Methods for Efficient Large Scale Video Labeling. arXiv:1706.04572 (2017).  M. Skalic M. Pekalski and X. Pan. 2017. Deep Learning Methods for Efficient Large Scale Video Labeling. arXiv:1706.04572 (2017)."},{"key":"e_1_3_2_2_46_1","unstructured":"N. Srivastava E. Mansimov and R. Salakhudinov. 2015. Unsupervised learning of video representations using lstms Proc. of the International Conference on Machine Learning.   N. Srivastava E. Mansimov and R. Salakhudinov. 2015. Unsupervised learning of video representations using lstms Proc. of the International Conference on Machine Learning."},{"key":"e_1_3_2_2_47_1","unstructured":"F. Strub J. Mary and R. Gaudel. 2016. Hybrid Collaborative Filtering with Neural Networks. CoRR Vol. abs\/1603.00806 (2016).  F. Strub J. Mary and R. Gaudel. 2016. Hybrid Collaborative Filtering with Neural Networks. CoRR Vol. abs\/1603.00806 (2016)."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/WI.2007.136"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/2964284.2967199"},{"key":"e_1_3_2_2_50_1","volume-title":"Proc. of the IEEE Conference on Computer Vision and Pattern Recognition.","author":"Szegedy C.","unstructured":"C. Szegedy , W. Liu , Y. Jia , P. Sermanet , S. Reed , D. Anguelov , D. Erhan , V. Vanhoucke , and A. Rabinovich . 2015. Going deeper with convolutions . In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. 2015. Going deeper with convolutions. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_3_2_2_51_1","unstructured":"A. van den Oord S. Dieleman and B. Schrauwen. 2013. Deep content-based music recommendation. In Advances in Neural Information Processing Systems.   A. van den Oord S. Dieleman and B. Schrauwen. 2013. Deep content-based music recommendation. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_52_1","unstructured":"M. Volkovs and R. Zemel. 2012. Collaborative ranking with 17 parameters. In Advances in Neural Information Processing Systems. 2294--2302.   M. Volkovs and R. Zemel. 2012. Collaborative ranking with 17 parameters. In Advances in Neural Information Processing Systems. 2294--2302."},{"key":"e_1_3_2_2_53_1","unstructured":"H. Wang T. Zhang and J. Wu. 2017. The Monkeytyping Solution to the YouTube-8M Video Understanding Challenge. arXiv:1706.05150 (2017).  H. Wang T. Zhang and J. Wu. 2017. 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Distance metric learning with application to clustering with side-information Advances in neural information processing systems."},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"crossref","unstructured":"H. Xiong and X. W. Chen. 2006. Kernel-based distance metric learning for microarray data classification. BMC bioinformatics Vol. 7 1 (2006) 299.  H. Xiong and X. W. Chen. 2006. Kernel-based distance metric learning for microarray data classification. BMC bioinformatics Vol. 7 1 (2006) 299.","DOI":"10.1186\/1471-2105-7-299"},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/2964284.2964297"},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/2671188.2749398"},{"key":"e_1_3_2_2_60_1","volume-title":"Beyond Short Snippets: Deep Networks for Video Classification Proc. of the IEEE Conference on Computer Vision and Pattern Recognition.","author":"Ng J. Yue-Hei","unstructured":"J. Yue-Hei Ng , M. Hausknecht , S. Vijayanarasimhan , O. Vinyals , R. 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