Recurrent Neural Network[Content]

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下面的RNN，LSTM，GRU模型圖來自這里
簡單的綜述

1. RNN

圖1.1 標准RNN模型的結構

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2. BiRNN

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3. LSTM

圖3.1 LSTM模型的結構

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4. Clockwork RNN

5. Depth Gated RNN

6. Grid LSTM

7. DRAW

8. RLVM

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9. GRU

圖9.1 GRU模型的結構

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10. NTM

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11. QRNN

圖11.1 f-pooling時候的QRNN結構圖

圖11.2 fo-pooling時候的QRNN結構圖

圖11.3 ifo-pooling時候的QRNN結構圖
點這里，QRNN

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12. Persistent RNN

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13. SRU

圖13.1 SRU模型的結構
點這里，SRU

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參考文獻：
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.. [survey] - Karpathy A, Johnson J, Fei-Fei L. Visualizing and understanding recurrent networks[J]. arXiv preprint arXiv:1506.02078, 2015.

1. [RNN] - Elman, Jeffrey L. “Finding structure in time.” Cognitive science 14.2 (1990): 179-211.
2. [BiRNN] - Schuster, Mike, and Kuldip K. Paliwal. “Bidirectional recurrent neural networks.” IEEE Transactions on Signal Processing 45.11 (1997): 2673-2681.
3. [LSTM] - Hochreiter, Sepp, and Jürgen Schmidhuber. “Long short-term memory.” Neural computation 9.8 (1997): 1735-1780
   .. [LSTM] - 理解 LSTM 網絡
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4. [Multi-dimensional RNN] - Alex Graves, Santiago Fernandez, and Jurgen Schmidhuber, Multi-Dimensional Recurrent Neural Networks, ICANN 2007
5. [GFRNN] - Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, Yoshua Bengio, Gated Feedback Recurrent Neural Networks, arXiv:1502.02367 / ICML 2015
6. [Tree-Structured RNNs] - Kai Sheng Tai, Richard Socher, and Christopher D. Manning, Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks, arXiv:1503.00075 / ACL 2015
   .. [Tree-Structured RNNs] - Samuel R. Bowman, Christopher D. Manning, and Christopher Potts, Tree-structured composition in neural networks without tree-structured architectures, arXiv:1506.04834
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8. [Depth Gated RNN] - Yao K, Cohn T, Vylomova K, et al. Depth-gated recurrent neural networks[J]. arXiv preprint, 2015.
9. [Grid LSTM] - Kalchbrenner N, Danihelka I, Graves A. Grid long short-term memory[J]. arXiv preprint arXiv:1507.01526, 2015.
10. [Segmental RNN] - Lingpeng Kong, Chris Dyer, Noah Smith, "Segmental Recurrent Neural Networks", ICLR 2016.
11. [**Seq2seq for Sets **] - Oriol Vinyals, Samy Bengio, Manjunath Kudlur, "Order Matters: Sequence to sequence for sets", ICLR 2016.
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13. [DRAW] - Gregor K, Danihelka I, Graves A, et al. DRAW: A recurrent neural network for image generation[J]. arXiv preprint arXiv:1502.04623, 2015.
14. [RLVM] - Chung J, Kastner K, Dinh L, et al. A recurrent latent variable model for sequential data[C]//Advances in neural information processing systems. 2015: 2980-2988.
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16. [GRU] - Cho K, Van Merriënboer B, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[J]. arXiv preprint arXiv:1406.1078, 2014.
    .. [GRU] - Cho K, Van Merriënboer B, Bahdanau D, et al. On the properties of neural machine translation: Encoder-decoder approaches[J]. arXiv preprint arXiv:1409.1259, 2014.
    .. [GRU] - Chung, Junyoung, et al. “Empirical evaluation of gated recurrent neural networks on sequence modeling.” arXiv preprint arXiv:1412.3555 (2014).
17. [NTM] - Graves, Alex, Greg Wayne, and Ivo Danihelka. “Neural turing machines.” arXiv preprint arXiv:1410.5401 (2014).
18. [Neural GPU] - Łukasz Kaiser, Ilya Sutskever, arXiv:1511.08228 / ICML 2016 (under review)
19. [QRNN] - Bradbury J, Merity S, Xiong C, et al. Quasi-recurrent neural networks[J]. arXiv preprint arXiv:1611.01576, 2016.
20. [Memory Network] - Jason Weston, Sumit Chopra, Antoine Bordes, Memory Networks, arXiv:1410.3916
21. [Pointer Network] - Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly, Pointer Networks, arXiv:1506.03134 / NIPS 2015
22. [Deep Attention Recurrent Q-Network] - Ivan Sorokin, Alexey Seleznev, Mikhail Pavlov, Aleksandr Fedorov, Anastasiia Ignateva, Deep Attention Recurrent Q-Network , arXiv:1512.01693
23. [Dynamic Memory Networks] - Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, Richard Socher, "Ask Me Anything: Dynamic Memory Networks for Natural Language Processing", arXiv:1506.07285
24. [SRU] - Lei T, Zhang Y. Training RNNs as Fast as CNNs[J]. arXiv preprint arXiv:1709.02755, 2017.
25. [知乎] - 如何評價新提出的RNN變種SRU
26. [attention] - Xu K, Ba J, Kiros R, et al. Show, attend and tell: Neural image caption generation with visual attention[C]//International Conference on Machine Learning. 2015: 2048-2057.
27. [Persistent RNN] - Diamos G, Sengupta S, Catanzaro B, et al. Persistent rnns: Stashing recurrent weights on-chip[C]//International Conference on Machine Learning. 2016: 2024-2033.
    .. [Persistent RNN] - Diamos G, Sengupta S, Catanzaro B, et al. Persistent RNNs: Stashing Weights on Chip[J]. 2016.
28. [github] - Awesome Recurrent Neural Networks.