{"id":8560,"date":"2016-11-07T01:00:00","date_gmt":"2016-11-06T17:00:00","guid":{"rendered":"http:\/\/swarma.org\/%swarma-paper%\/"},"modified":"2018-12-06T00:48:49","modified_gmt":"2018-12-05T16:48:49","slug":"%e6%b7%b1%e5%ba%a6%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%e8%af%bb%e4%b9%a6%e4%bc%9a-%e4%bd%a0%e6%95%a2%e6%9d%a5%e5%90%97%ef%bc%9f","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=8560","title":{"rendered":"\u6df1\u5ea6\u201c\u6df1\u5ea6\u5b66\u4e60\u201d\u8bfb\u4e66\u4f1a | \u4f60\u6562\u6765\u5417\uff1f"},"content":{"rendered":"
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      <\/strong><\/p>\n<\/li>\n<\/ul>\n

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      \u65f6\u95f4<\/span><\/strong>\uff1a2016\/11\/13-2017\/04\/30 <\/strong>\u6bcf\u4e24\u5468\u4e00\u671f\uff0c\u517110\u671f<\/span><\/p>\n

      \u5730\u70b9<\/span><\/strong>\uff1a\u5317\u4eac\uff08\u5177\u4f53\u5730\u5740\u89c1\u8bfb\u4e66\u4f1a\u7fa4\u901a\u77e5\uff09<\/span><\/p>\n

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      <\/span><\/strong><\/span><\/p>\n<\/section>\n<\/blockquote>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

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      1. \n
        l Krizhevsky A, Sutskever I, Hinton G E. ImageNet Classification with Deep Convolutional Neural Networks[J]. Advances in Neural Information Processing Systems, 2012, 25(2):2012.<\/span><\/section>\n<\/li>\n
      2. \n
        l Kalchbrenner N, Grefenstette E, Blunsom P. A Convolutional Neural Network for Modelling Sentences[J]. Eprint Arxiv, 2014, 1.<\/span><\/section>\n<\/li>\n
      3. \n
        l Alexey Dosovitskiy, Thomas Brox. Inverting Visual Representations with Convolutional Networks. 2015. arXiv preprint.<\/span><\/section>\n<\/li>\n
      4. \n
        l Michael Edwards and Xianghua Xie: Graph Convolutional Neural Network, http:\/\/www.bmva.org\/bmvc\/2016\/papers\/paper114\/paper114.pdf<\/span><\/section>\n<\/li>\n
      5. \n
        l Alexey Dosovitskiy, Thomas Brox. Inverting Visual Representations with Convolutional Networks. 2015. arXiv preprint.<\/span><\/section>\n<\/li>\n
      6. \n
        l Lipton Z C, Berkowitz J, Elkan C. A Critical Review of Recurrent Neural Networks for Sequence Learning[J]. Computer Science, 2015.<\/span><\/section>\n<\/li>\n
      7. \n
        l Andrej Karpathy, Justin Johnson, Li Fei-Fei. Visualizing and Understanding Recurrent Networks. NIPS 2015.<\/span><\/section>\n<\/li>\n
      8. \n
        l A Graves, G Wayne: Hybrid computing using a neural network with dynamic external memory, Nature 2016<\/span><\/section>\n<\/li>\n
      9. \n
        l Andrychowicz, M., & Kurach, K. (2016). Learning Efficient Algorithms with Hierarchical Attentive Memory. arXiv preprint arXiv:1602.03218.<\/span><\/section>\n<\/li>\n
      10. \n
        l Graves, A., Wayne, G., & Danihelka, I. (2014). Neural turing machines. arXiv preprint arXiv:1410.5401.<\/span><\/section>\n<\/li>\n
      11. \n
        l Graves, A. (2016). Adaptive Computation Time for Recurrent Neural Networks. arXiv preprint arXiv:1603.08983.<\/span><\/section>\n<\/li>\n
      12. \n
        l Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R.S. & Bengio, Y., 2015. (2015). Show, attend and tell: Neural image caption generation with visual attention. arXiv preprint arXiv:1502.03044, 2(3), 5.<\/span><\/section>\n<\/li>\n
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        l Zaremba, W., & Sutskever, I. (2015). Reinforcement learning neural Turing machines. arXiv preprint arXiv:1505.00521, 362.<\/span><\/section>\n<\/li>\n
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        l Kumar, A., Irsoy, O., Su, J., Bradbury, J., English, R., Pierce, B., Ondruska, P., Gulrajani, I. & Socher, R., (2015). Ask me anything: Dynamic memory networks for natural language processing. arXiv preprint arXiv:1506.07285.<\/span><\/section>\n<\/li>\n
      15. \n
        l Kurach, K., Andrychowicz, M., & Sutskever, I. (2015). Neural random-access machines. arXiv preprint arXiv:1511.06392.<\/span><\/section>\n<\/li>\n
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        l Neelakantan, A., Le, Q. V., & Sutskever, I. (2015). Neural programmer: Inducing latent programs with gradient descent. arXiv preprint arXiv:1511.04834.<\/span><\/section>\n<\/li>\n
      17. \n
        l Olah, C. (2015). Understanding LSTM Networks.<\/span><\/section>\n<\/li>\n
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        l Geoffrey Hinton, Oriol Vinyals, Jeff Dean. Distilling the Knowledge in a Neural Network. NIPS 2014.<\/span><\/section>\n<\/li>\n
      19. \n
        l Goodfellow I, Pougetabadie J, Mirza M, et al. Generative Adversarial Nets[J]. Advances in Neural Information Processing Systems, 2014:2672-2680.<\/span><\/section>\n<\/li>\n
      20. \n
        l Bryan Perozzi, Rami Al-Rfou’, Steven Skiena. DeepWalk: online learning of social representations. KDD 2014.<\/span><\/section>\n<\/li>\n
      21. \n
        l Memory Network: https:\/\/arxiv.org\/abs\/1410.3916<\/span><\/section>\n<\/li>\n
      22. \n
        l + End-to-End Memory Networks: https:\/\/arxiv.org\/abs\/1503.08895<\/span><\/section>\n<\/li>\n
      23. \n
        l + Towards ai-complete question answering: https:\/\/arxiv.org\/abs\/1502.05698<\/span><\/section>\n<\/li>\n
      24. \n
        l + Large-scale Simple Question Answering with Memory Networks: https:\/\/arxiv.org\/abs\/1506.02075<\/span><\/section>\n<\/li>\n
      25. \n
        l + Ask me anyting: Dynamic Memory Networks for Natural Language Processing: https:\/\/arxiv.org\/abs\/1506.07285<\/span><\/section>\n<\/li>\n
      26. \n
        l + The Goldlocks Principle: Reading Children’s Book with Explicit Memory Representations: https:\/\/arxiv.org\/abs\/1511.02301<\/span><\/section>\n<\/li>\n
      27. \n
        l + Dialog-based Language Learning: https:\/\/arxiv.org\/abs\/1604.06045<\/span><\/section>\n<\/li>\n
      28. \n
        l + Learning End-to-End Goal-Oriented Dialog: https:\/\/arxiv.org\/abs\/1605.07683<\/span><\/section>\n<\/li>\n
      29. \n
        l + Key-Value Memory Networks for Directly Reading Documents: https:\/\/arxiv.org\/abs\/1606.03126<\/span><\/section>\n<\/li>\n
      30. \n
        l + Hierarchical Memory Networks for Answer Selection on Unknown Words: https:\/\/arxiv.org\/abs\/1609.08843<\/span><\/section>\n<\/li>\n<\/ol>\n


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        <\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n
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