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587 ネットワークの表現学習 1 1 1 1 Deep Learning [1] Google [2] Deep Learning [3] [4] 2014 Deepwalk [5] 1 2 [6] [7] [8] 1 2 1 word2vec[9] word2vec 1 http://www.ai-gakkai.or.jp/my-bookmark_vol31-no4

588 31 4 2016 7 word2vec [10] [11] 2014 DeepWalk word2vec DeepWalk word2vec word2vec DeepWalk Spectral Clustering [12] 1 2 LINE [13] GraRep [14] DeepWalk KDD WWW Web [52, 53, 54] [15] 2 2 1 [16] word2vec word2vec word2vec paragraph to vec [17] Sequence to Sequnce [18] [19] LDA Latent Dirichlet Allocation word2vec [20] LDA Spectral Clustering word2vec [21] word to vec doc2vec [22] paragraph to vec Sequence to sequence learning with neural networks [23] seq to seq Deep learning Nature 2015 [24] Deep Learning Yosua Bengio

589 Vol. 2 [25] PLSA word2vec [26] word2vec Geoffrey E. Hinton [27] Yoshua Bengio [28] Deep Learning Tomas Mikolov [29] word2vec doc2vec, seq2seq Google Deep Mind [30] AI DQN Deep Q-Network Facebook AI Research FAIR [31] Google Microsoft Research [32] Facebook Google ICIR representation Deep Learning DNN ICIR [33] International Conference on Learning Representations EMNLP [34] Conference on Empirical Methods in Natural Language Processing ACL [35] Annual Meeting of the Association for Computational Linguistics COLING [36] International Conference on Computational Linguistics ACL-IJCNLP [37] International Joint Conference on Natural Language Processing ACM SIGKDD [38] ACM SIGKDD Conference on Knowledge Discovery and Data Mining ICML [39] International Conference on Machine Learning AAAI [40] Conference AAAI Conference IJCAI [41] International Joint Conferences on Artificial Intelligence AAAI WWW [42] ACM World Wide Web Conference Web CIKM [43] International Conference on Machine Learning ICML

590 31 4 2016 7 NIPS [44] Annual Conference on Neural Information Processing System Deep Learning Python Gensim [45] Python word2vec doc2vec Tenser Flow [46] Deep Learning 1 Google Chainer [47] Deep Learning PFI 2 2 WWW 2015 LINE [48] word2vec 1 2 2 DeepWalk LINE PTE Google Scholar Knowledge Graph Knowledge Graph Knowledge Graph DeepWalk [49] 2 word2vec LINE [50] DeepWalk DeepWalk GraRep [51]

591 PTE [52] LINE Heterogeneous Network Embedding [53] text-associated DeepWalk [54] Visualizing Large-scale and High-dimensional Data [55] Network Embedding WWW 2016 LINE Knowledge Graph Knowledge Graph [56] word net Neural Network Web Knowledge Graph Jian Tang [57] LINE PTE Microsoft Research Asia Bryan Perozzi [58] DeepWalk Ph. D. Candiate Barabási [59] BA [60] Deepwalk [61] Python Python LINE [62] C LINUX Visualizing Large-scale and High-dimensional Data [63] Network Embedding 2 1

592 31 4 2016 7 network and embedding network and representation AI [64] CREST [65] 3 [66] Acknowledgement [1] http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html [2] http://derivecv.tumblr.com/post/53021563144 [3] http://arxiv.org/abs/1508.06576 [4] https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neuralnetworks.pdf [5] http://dl.acm.org/citation.cfm?id=2623732 [6] http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/vinyals_show_and_ Tell_2015_CVPR_paper.html [7] http://arxiv.org/abs/1511.02793 [8] https://www.tensorflow.org/versions/r0.8/tutorials/seq2seq/index.html [9] http://papers.nips.cc/paper/5021-di [10] http://link.springer.com/chapter/10.1007/978-94-017-6059-1_36 [11] http://papers.nips.cc/paper/5477-scalable-non-linear-learning-with-adaptivepolynomial-expansions [12] http://link.springer.com/article/10.1007/s10618-010-0210-x [13] http://dl.acm.org/citation.cfm?id=2741093 [14] http://dl.acm.org/citation.cfm?id=2806512 [15] http://dl.acm.org/citation.cfm?id=2783296 [16] http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf [17] http://cs.stanford.edu/~quocle/paragraph_vector.pdf [18] https://www.tensorflow.org/versions/r0.8/tutorials/seq2seq/index.html [19] http://arxiv.org/pdf/1411.3315.pdf [20] https://www.quora.com/whats-the-difference-between-distributed-anddistributional-semantic-representations [21] http://deeplearning4j.org/word2vec

593 [22] http://cs.stanford.edu/~quocle/paragraph_vector.pdf [23] http://papers.nips.cc/paper/5346-information-based-learning-by-agents-inunbounded-state-spaces [24] http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html [25] http://www.amazon.co.jp/dp/4000298526 [26] http://www.ai-gakkai.or.jp/vol31_no2/ [27] http://www.cs.toronto.edu/~hinton [28] http://www.iro.umontreal.ca/~bengioy/yoshua_en/index.html [29] https://scholar.google.com/citations?user=obu8kmmaaaaj&hl=en [30] http://deepmind.com/ [31] http://research.facebook.com/ai [32] http://research.microsoft.com/ [33] http://www.iclr.cc/doku.php?id=iclr2016:main [34] http://www.emnlp2016.net/ [35] http://acl2016.org/ [36] http://coling2016.anlp.jp/ [37] http://acl2015.org/ [38] http://www.kdd.org/kdd2016/ [39] http://icml.cc/2016/ [40] http://www.aaai.org/conferences/aaai/aaai16.php [41] http://ijcai-16.org/ [42] http://cikm2016.cs.iupui.edu/ [43] http://cikm2016.cs.iupui.edu/ [44] https://nips.cc/ [45] https://radimrehurek.com/gensim/ [46] https://www.tensorflow.org/ [47] http://chainer.org/ [48] http://dl.acm.org/citation.cfm?id=2741093 [49] http://dl.acm.org/citation.cfm?id=2623732 [50] http://dl.acm.org/citation.cfm?id=2741093 [51] http://dl.acm.org/citation.cfm?id=2806512 [52] http://dl.acm.org/citation.cfm?id=2783307 [53] http://dl.acm.org/citation.cfm?id=2783296 [54] http://dl.acm.org/citation.cfm?id=2832542 [55] http://dl.acm.org/citation.cfm?id=2883041 [56] https://arxiv.org/abs/1503.00759 [57] https://arxiv.org/find/cs/1/au:+tang_j/0/1/0/all/0/1 [58] http://perozzi.net/ [59] http://barabasi.com/ [60] http://science.sciencemag.org/content/286/5439/509 [61] http://github.com/phanein/deepwalk [62] http://github.com/tangjianpku/line [63] https://github.com/elbamos/largevis [64] https://unit.aist.go.jp/airc/ [65] http://www.jst.go.jp/kisoken/crest/project/44/15656596.html [66] https://googleblog.blogspot.jp/2012/06/using-large-scale-brain-simulations-for. html