49 papers

README.md

@@ -116,10 +116,41 @@ I would continue adding papers to this roadmap.
 
 ## 2.5 Neural Turing Machine
 
+**[36]** Graves, Alex, Greg Wayne, and Ivo Danihelka. "**Neural turing machines**." arXiv preprint arXiv:1410.5401 (2014). [[pdf]](http://arxiv.org/pdf/1410.5401.pdf) **(Basic Prototype of Future Computer)** :star::star::star::star::star:
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+**[37]** Zaremba, Wojciech, and Ilya Sutskever. "**Reinforcement learning neural Turing machines**." arXiv preprint arXiv:1505.00521 362 (2015). [[pdf]](https://pdfs.semanticscholar.org/f10e/071292d593fef939e6ef4a59baf0bb3a6c2b.pdf) :star::star::star:
+
+**[38]** Weston, Jason, Sumit Chopra, and Antoine Bordes. "**Memory networks**." arXiv preprint arXiv:1410.3916 (2014).[[pdf]](http://arxiv.org/pdf/1410.3916) :star::star::star:
+
+
+**[39]** Sukhbaatar, Sainbayar, Jason Weston, and Rob Fergus. "**End-to-end memory networks**." Advances in neural information processing systems. 2015. [[pdf]](http://papers.nips.cc/paper/5846-end-to-end-memory-networks.pdf) :star::star::star::star:
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+**[40]** Vinyals, Oriol, Meire Fortunato, and Navdeep Jaitly. "**Pointer networks**." Advances in Neural Information Processing Systems. 2015. [[pdf]](http://papers.nips.cc/paper/5866-pointer-networks.pdf) :star::star::star::star:
+
+**[41]** Graves, Alex, et al. "**Hybrid computing using a neural network with dynamic external memory**." Nature (2016). [[pdf]](https://www.dropbox.com/s/0a40xi702grx3dq/2016-graves.pdf) **(Milestone,combine above papers' ideas)** :star::star::star::star::star:
+
 ## 2.6 Deep Reinforcement Learning
 
+**[42]** Mnih, Volodymyr, et al. "**Playing atari with deep reinforcement learning**." arXiv preprint arXiv:1312.5602 (2013). [[pdf]](http://arxiv.org/pdf/1312.5602.pdf)) **(First Paper named deep reinforcement learning)** :star::star::star::star:
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+**[43]** Mnih, Volodymyr, et al. "**Human-level control through deep reinforcement learning**." Nature 518.7540 (2015): 529-533. [[pdf]](http://www.davidqiu.com:8888/research/nature14236.pdf) **(Milestone)** :star::star::star::star::star:
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+**[44]** Wang, Ziyu, Nando de Freitas, and Marc Lanctot. "**Dueling network architectures for deep reinforcement learning**." arXiv preprint arXiv:1511.06581 (2015). [[pdf]](http://arxiv.org/pdf/1511.06581) **(ICLR best paper,great idea)**  :star::star::star::star:
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+**[45]** Mnih, Volodymyr, et al. "**Asynchronous methods for deep reinforcement learning**." arXiv preprint arXiv:1602.01783 (2016). [[pdf]](http://arxiv.org/pdf/1602.01783) **(State-of-the-art method)** :star::star::star::star::star:
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+**[46]** Lillicrap, Timothy P., et al. "**Continuous control with deep reinforcement learning**." arXiv preprint arXiv:1509.02971 (2015). [[pdf]](http://arxiv.org/pdf/1509.02971) **(DDPG)** :star::star::star::star:
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+**[47]** Gu, Shixiang, et al. "**Continuous Deep Q-Learning with Model-based Acceleration**." arXiv preprint arXiv:1603.00748 (2016). [[pdf]](http://arxiv.org/pdf/1603.00748) **(NAF)** :star::star::star::star:
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+**[48]** Schulman, John, et al. "**Trust region policy optimization**." CoRR, abs/1502.05477 (2015). [[pdf]](http://www.jmlr.org/proceedings/papers/v37/schulman15.pdf) **(TRPO)** :star::star::star::star:
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+**[49]** Silver, David, et al. "**Mastering the game of Go with deep neural networks and tree search**." Nature 529.7587 (2016): 484-489. [[pdf]](http://willamette.edu/~levenick/cs448/goNature.pdf) **(AlphaGo)** :star::star::star::star::star:
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 ## 2.7 Deep Transfer Learning
 
+## 2.8 One Shot Learning
+
 # 3 Applications
 
 ## 3.1 NLP(Natural Language Processing)
@@ -132,13 +163,15 @@ I would continue adding papers to this roadmap.
 
 ## 3.5 Machine Translation
 
-## 3.6 Art
+## 3.6 Audio
+
+## 3.7 Art
 
-## 3.7 Game
+## 3.8 Game
 
-## 3.8 Robotics
+## 3.9 Robotics
 
-## 3.9 Other Frontiers
+## 3.10 Other Frontiers