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Don't count predict! A systematic comparison of context-counting vs. context-predicting semantic vectors.. In ACL (1). 238--247.  Marco Baroni Georgiana Dinu and Germ\u00e1n Kruszewski . 2014. Don't count predict! A systematic comparison of context-counting vs. context-predicting semantic vectors.. In ACL (1). 238--247.","DOI":"10.3115\/v1\/P14-1023"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"e_1_3_2_1_6_1","unstructured":"Yoshua Bengio Li Yao Guillaume Alain and Pascal Vincent . 2013 b. Generalized denoising auto-encoders as generative models Advances in Neural Information Processing Systems. 899--907.   Yoshua Bengio Li Yao Guillaume Alain and Pascal Vincent . 2013 b. 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Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome research Vol. 26, 7 (2016), 990--999."},{"key":"e_1_3_2_1_16_1","volume-title":"Danilo Jimenez Rezende, and Max Welling","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma , Shakir Mohamed , Danilo Jimenez Rezende, and Max Welling . 2014 . Semi-supervised learning with deep generative models Advances in Neural Information Processing Systems . 3581--3589. Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling . 2014. Semi-supervised learning with deep generative models Advances in Neural Information Processing Systems. 3581--3589."},{"key":"e_1_3_2_1_17_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling . 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 ( 2013 ). 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Anshul Kundaje, Wouter Meuleman, Jason Ernst, Misha Bilenky, Angela Yen, Alireza Heravi-Moussavi, Pouya Kheradpour, Zhizhuo Zhang, Jianrong Wang, Michael J Ziller, et almbox. . 2015. Integrative analysis of 111 reference human epigenomes. Nature Vol. 518, 7539 (2015), 317--330."},{"key":"e_1_3_2_1_20_1","volume-title":"Deep learning. Nature","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun , Yoshua Bengio , and Geoffrey Hinton . 2015. Deep learning. Nature Vol. 521 , 7553 ( 2015 ), 436--444. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton . 2015. Deep learning. Nature Vol. 521, 7553 (2015), 436--444."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_23_1","volume-title":"A method to predict the impact of regulatory variants from DNA sequence. 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Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 ( 2013 ). Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean . 2013 a. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)."},{"key":"e_1_3_2_1_28_1","unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean . 2013 b. Distributed representations of words and phrases and their compositionality Advances in neural information processing systems. 3111--3119.   Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean . 2013 b. Distributed representations of words and phrases and their compositionality Advances in neural information processing systems. 3111--3119."},{"key":"e_1_3_2_1_29_1","volume-title":"International Conference on Machine Learning. 2368--2376","author":"Pezeshki Mohammad","year":"2016","unstructured":"Mohammad Pezeshki , Linxi Fan , Philemon Brakel , Aaron Courville , and Yoshua Bengio . 2016 . 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Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape. Nature biotechnology Vol. 32, 2 (2014), 171--178."},{"key":"e_1_3_2_1_34_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman . 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 ( 2014 ). Karen Simonyan and Andrew Zisserman . 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_1_36_1","volume-title":"et almbox","author":"Thurman Robert E","year":"2012","unstructured":"Robert E Thurman , Eric Rynes , Richard Humbert , Jeff Vierstra , Matthew T Maurano , Eric Haugen , Nathan C Sheffield , Andrew B Stergachis , Hao Wang , Benjamin Vernot , et almbox . . 2012 . The accessible chromatin landscape of the human genome. Nature Vol . 489, 7414 (2012), 75--82. Robert E Thurman, Eric Rynes, Richard Humbert, Jeff Vierstra, Matthew T Maurano, Eric Haugen, Nathan C Sheffield, Andrew B Stergachis, Hao Wang, Benjamin Vernot, et almbox. . 2012. The accessible chromatin landscape of the human genome. Nature Vol. 489, 7414 (2012), 75--82."},{"key":"e_1_3_2_1_37_1","volume-title":"From neural PCA to deep unsupervised learning. Advances in Independent Component Analysis and Learning Machines","author":"Harri Valpola","year":"2015","unstructured":"Harri Valpola . 2015. From neural PCA to deep unsupervised learning. Advances in Independent Component Analysis and Learning Machines ( 2015 ), 143--171. Harri Valpola . 2015. From neural PCA to deep unsupervised learning. Advances in Independent Component Analysis and Learning Machines (2015), 143--171."},{"volume-title":"Neural Networks: Tricks of the Trade","author":"Weston Jason","key":"e_1_3_2_1_38_1","unstructured":"Jason Weston , Fr\u00e9d\u00e9ric Ratle , Hossein Mobahi , and Ronan Collobert . 2012. Deep learning via semi-supervised embedding . In Neural Networks: Tricks of the Trade . Springer , 639--655. Jason Weston, Fr\u00e9d\u00e9ric Ratle, Hossein Mobahi, and Ronan Collobert . 2012. Deep learning via semi-supervised embedding. In Neural Networks: Tricks of the Trade. Springer, 639--655."},{"volume-title":"Visualizing and understanding convolutional networks European conference on computer vision","author":"Zeiler Matthew D","key":"e_1_3_2_1_39_1","unstructured":"Matthew D Zeiler and Rob Fergus . 2014. Visualizing and understanding convolutional networks European conference on computer vision . Springer , 818--833. Matthew D Zeiler and Rob Fergus . 2014. Visualizing and understanding convolutional networks European conference on computer vision. Springer, 818--833."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btw255"},{"key":"e_1_3_2_1_41_1","volume-title":"Predicting effects of noncoding variants with deep learning-based sequence model. Nature methods","author":"Zhou Jian","year":"2015","unstructured":"Jian Zhou and Olga G Troyanskaya . 2015. Predicting effects of noncoding variants with deep learning-based sequence model. Nature methods Vol. 12 , 10 ( 2015 ), 931--934. 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