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人工智能研究专家 Flood Sung 针对近几年深度学习的研究进展提供了一个非常详

细的阅读清单。如果你在深度学习领域是一个新手，你可以会想知道如何从哪篇论

文开始阅读学习，人工智能研究专家 Flood Sung 制定了这一份详细的 paper list，

包括深度学习历史和基础知识、深度学习方法（涉及模型、优化、无监督学习、

RNN、深度强化学习等）、深度学习应用（自然语言处理、目标检测、视觉跟

踪、图像描述生成、机器翻译、机器人、目标分割等），建议你收藏，仔细学习。

Github 地址：

https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap

▌目录

1 深度学习历史和基础知识（Deep Learning History and Basics）

1.0 Book

1.1 Survey

1.2 Deep Belief Network(DBN)(Milestone of Deep Learning Eve)

1.3 ImageNet Evolution（Deep Learning broke out from here）

1.4 Speech Recognition Evolution

2 深度学习方法（Deep Learning Method）

2.1 Model

2.2 Optimization

2.3 Unsupervised Learning / Deep Generative Model

2.4 RNN / Sequence-to-Sequence Model

2.5 Neural Turing Machine

2.6 Deep Reinforcement Learning

2.7 Deep Transfer Learning / Lifelong Learning / especially for RL

2.8 One Shot Deep Learning

3 应用（Applications）

[8] Hinton, Geoffrey, et al. "Deep neural networks for acoustic modeling in

speech recognition: The shared views of four research groups." IEEE Signal

Processing Magazine 29.6 (2012): 82-97. [pdf] (Breakthrough in speech

recognition)

链接：http://cs224d.stanford.edu/papers/maas_paper.pdf

[9] Graves, Alex, Abdel-rahman Mohamed, and Geoffrey Hinton. "Speech

recognition with deep recurrent neural networks." 2013 IEEE international

conference on acoustics, speech and signal processing. IEEE, 2013. [pdf] (RNN)

链接：http://arxiv.org/pdf/1303.5778.pdf

[10] Graves, Alex, and Navdeep Jaitly. "Towards End-To-End Speech

Recognition with Recurrent Neural Networks." ICML. Vol. 14. 2014. [pdf]

链接：http://www.jmlr.org/proceedings/papers/v32/graves14.pdf

[11] Sak, Haşim, et al. "Fast and accurate recurrent neural network acoustic

models for speech recognition." arXiv preprint arXiv:1507.06947

(2015). [pdf] (Google Speech Recognition System)

链接：http://arxiv.org/pdf/1507.06947

[12] Amodei, Dario, et al. "Deep speech 2: End-to-end speech recognition in

english and mandarin." arXiv preprint arXiv:1512.02595 (2015). [pdf] (Baidu

Speech Recognition System)

链接：https://arxiv.org/pdf/1512.02595.pdf

[13] W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, G.

Zweig "Achieving Human Parity in Conversational Speech Recognition."

arXiv preprint arXiv:1610.05256 (2016). [pdf] (State-of-the-art in speech

recognition, Microsoft)

链接：https://arxiv.org/pdf/1610.05256v1

After reading above papers, you will have a basic understanding of the Deep

Learning history, the basic architectures of Deep Learning model(including

CNN, RNN, LSTM) and how deep learning can be applied to image and

speech recognition issues. The following papers will take you in-depth

understanding of the Deep Learning method, Deep Learning in different areas

of application and the frontiers. I suggest that you can choose the following

papers based on your interests and research direction.

2 深度学习方法（Deep Learning Method）

2.1 Model

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WP8733684

2018-02-12

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