如何开启深度学习之旅?这三大类125篇论文为你导航(附资源下载)

如果你现在还是个深度学习的新手,那么你问的第一个问题可能是「我应该从哪篇文章开始读呢?」在 Github 上,songrotek 准备了一套深度学习阅读清单,而且这份清单在随时更新。至于文中提到的 PDF,读者们可点击阅读原文下载机器之心打包的论文,或点开下面的项目地址下载自己喜欢的学习材料。


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


这份清单依照下述 4 条原则建立:


  • 从整体轮廓到细节

  • 从过去到当代

  • 从一般到具体领域

  • 聚焦当下最先进技术


你会发现很多非常新但很值得一读的论文。这份清单我会持续更新。


1、深度学习的历史与基础知识

1.0 书籍


[0] Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. 深度学习(Deep learning), An MIT Press book. (2015). (这是深度学习领域的圣经,你可以在读此书的同时阅读下面的论文)。


1.1 调查类:


[1] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. 深度学习 (Deep learning), Nature 521.7553 (2015): 436-444. (深度学习三位大牛对各种学习模型的评价)


1.2 深度信念网络(DBN)(深度学习前夜的里程碑)


[2] Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. 一个关于深度信念网络的快速学习算法(A fast learning algorithm for deep belief nets), (深度学习的前夜)


[3] Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. 使用神经网络降低数据的维度(Reducing the dimensionality of data with neural networks),  (里程碑式的论文,展示了深度学习的可靠性)


1.3 ImageNet 的演化(深度学习从这里开始)


[4] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. 使用深度卷积神经网络进行 ImageNet 分类任务(Imagenet classification with deep convolutional neural networks)(AlexNet, 深度学习的突破)


[5] Simonyan, Karen, and Andrew Zisserman. 针对大尺度图像识别工作的的超深卷积网络(Very deep convolutional networks for large-scale image recognition) (VGGNet, 神经网络开始变得非常深!)


[6] Szegedy, Christian, et al. 更深的卷积(Going deeper with convolutions)(GoogLeNet)


[7] He, Kaiming, et al. 图像识别的深度残差学习(Deep residual learning for image recognition)(ResNet,超级超级深的深度网络!CVPR--IEEE 国际计算机视觉与模式识别会议-- 最佳论文)


1.4 语音识别的演化


[8] Hinton, Geoffrey, et al. 语音识别中深度神经网络的声学建模(Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups)(语音识别中的突破)


[9] Graves, Alex, Abdel-rahman Mohamed, and Geoffrey Hinton. 用深度循环神经网络进行语音识别(Speech recognition with deep recurrent neural networks)(RNN)


[10] Graves, Alex, and Navdeep Jaitly. 面向端到端语音识别的循环神经网络(Towards End-To-End Speech Recognition with Recurrent Neural Networks)


[11] Sak, Haşim, et al. 语音识别中快且精准的循环神经网络声学模型(Fast and accurate recurrent neural network acoustic models for speech recognition)(谷歌语音识别系统)


[12] Amodei, Dario, et al. Deep speech 2:英语和汉语的端到端语音识别(Deep speech 2: End-to-end speech recognition in english and mandarin)(百度语音识别系统)


[13] W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, G. Zweig,在对话语音识别中实现人类平等(Achieving Human Parity in Conversational Speech Recognition) (最先进的语音识别技术,微软)


当你读完了上面给出的论文,你会对深度学习历史有一个基本的了解,深度学习建模的基本架构(包括了 CNN,RNN,LSTM)以及深度学习如何可以被应用于图像和语音识别问题。下面的论文会让你对深度学习方法,不同应用领域中的深度学习技术和其局限有深度认识。我建议你可以基于自己的兴趣和研究方向选择下面这些论文。


2 深度学习方法


2.1 模型


[14] Hinton, Geoffrey E., et al. 通过避免特征检测器的共适应来改善神经网络(Improving neural networks by preventing co-adaptation of feature detectors)(Dropout)


[15] Srivastava, Nitish, et al. Dropout:一种避免神经网络过度拟合的简单方法(Dropout: a simple way to prevent neural networks from overfitting)


[16] Ioffe, Sergey, and Christian Szegedy. Batch normalization:通过减少内部协变量加速深度网络训练(Batch normalization: Accelerating deep network training by reducing internal covariate shift)(2015 年一篇杰出论文)


[17] Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton.层归一化(Layer normalization)(批归一化的升级版)


[18] Courbariaux, Matthieu, et al. 二值神经网络:训练神经网络的权重和激活约束到正 1 或者负 1(Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to+ 1 or−1)(新模型,快)


[19] Jaderberg, Max, et al. 使用合成梯度的解耦神经接口(Decoupled neural interfaces using synthetic gradients)(训练方法的发明,令人惊叹的文章)


[20] Chen, Tianqi, Ian Goodfellow, and Jonathon Shlens. Net2net:通过知识迁移加速学习(Net2net: Accelerating learning via knowledge transfer) (修改之前的训练网络以减少训练)


[21] Wei, Tao, et al. 网络形态(Network Morphism)(修改之前的训练网络以减少训练 epoch)


2.2 优化


[22] Sutskever, Ilya, et al. 有关深度学习中初始化与动量因子的研究(On the importance of initialization and momentum in deep learning) (动量因子优化器)


[23] Kingma, Diederik, and Jimmy Ba. Adam:随机优化的一种方法(Adam: A method for stochastic optimization)(可能是现在用的最多的一种方法)


[24] Andrychowicz, Marcin, et al. 通过梯度下降学习梯度下降(Learning to learn by gradient descent by gradient descent) (神经优化器,令人称奇的工作)


[25] Han, Song, Huizi Mao, and William J. Dally. 深度压缩:通过剪枝、量子化训练和霍夫曼代码压缩深度神经网络(Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding) (ICLR 最佳论文,来自 DeePhi 科技初创公司,加速 NN 运行的新方向)


[26] Iandola, Forrest N., et al. SqueezeNet:带有 50x 更少参数和小于 1MB 模型大小的 AlexNet-层级精确度(SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size.) (优化 NN 的另一个新方向,来自 DeePhi 科技初创公司)


2.3 无监督学习/深度生成模型


[27] Le, Quoc V. 通过大规模无监督学习构建高级特征(Building high-level features using large scale unsupervised learning.) (里程碑,吴恩达,谷歌大脑,猫)


[28] Kingma, Diederik P., and Max Welling. 自动编码变异贝叶斯(Auto-encoding variational bayes.) (VAE) 


[29] Goodfellow, Ian, et al. 生成对抗网络(Generative adversarial nets.)(GAN, 超酷的想法)


[30] Radford, Alec, Luke Metz, and Soumith Chintala. 带有深度卷曲生成对抗网络的无监督特征学习(Unsupervised representation learning with deep convolutional generative adversarial networks.)(DCGAN)


[31] Gregor, Karol, et al. DRAW:一个用于图像生成的循环神经网络(DRAW: A recurrent neural network for image generation.) (值得注意的 VAE,杰出的工作)


[32] Oord, Aaron van den, Nal Kalchbrenner, and Koray Kavukcuoglu. 像素循环神经网络(Pixel recurrent neural networks.)(像素 RNN)


[33] Oord, Aaron van den, et al. 使用像素 CNN 解码器有条件地生成图像(Conditional image generation with PixelCNN decoders.) (像素 CNN)


2.4 RNN/序列到序列模型


[34] Graves, Alex. 带有循环神经网络的生成序列(Generating sequences with recurrent neural networks.)(LSTM, 非常好的生成结果,展示了 RNN 的力量)


[35] Cho, Kyunghyun, et al. 使用 RNN 编码器-解码器学习词组表征用于统计机器翻译(Learning phrase representations using RNN encoder-decoder for statistical machine translation.) (第一个序列到序列论文)


[36] Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. 运用神经网路的序列到序列学习(Sequence to sequence learning with neural networks.」)(杰出的工作)


[37] Bahdanau, Dzmitry, KyungHyun Cho, and Yoshua Bengio. 通过共同学习来匹配和翻译神经机器翻译(Neural Machine Translation by Jointly Learning to Align and Translate.)


[38] Vinyals, Oriol, and Quoc Le. 一个神经对话模型(A neural conversational model.)(聊天机器人上的序列到序列)


2.5 神经图灵机


[39] Graves, Alex, Greg Wayne, and Ivo Danihelka. 神经图灵机器(Neural turing machines.)arXiv preprint arXiv:1410.5401 (2014). (未来计算机的基本原型)


[40] Zaremba, Wojciech, and Ilya Sutskever. 强化学习神经图灵机(Reinforcement learning neural Turing machines.)


[41] Weston, Jason, Sumit Chopra, and Antoine Bordes. 记忆网络(Memory networks.)


[42] Sukhbaatar, Sainbayar, Jason Weston, and Rob Fergus. 端到端记忆网络(End-to-end memory networks.)


[43] Vinyals, Oriol, Meire Fortunato, and Navdeep Jaitly. 指示器网络(Pointer networks.)


[44] Graves, Alex, et al. 使用带有动力外部内存的神经网络的混合计算(Hybrid computing using a neural network with dynamic external memory.)(里程碑,结合上述论文的思想)


2.6 深度强化学习


[45] Mnih, Volodymyr, et al. 使用深度强化学习玩 atari 游戏(Playing atari with deep reinforcement learning.) (第一篇以深度强化学习命名的论文)


[46] Mnih, Volodymyr, et al. 通过深度强化学习达到人类水准的控制(Human-level control through deep reinforcement learning.) (里程碑)


[47] Wang, Ziyu, Nando de Freitas, and Marc Lanctot. 用于深度强化学习的决斗网络架构(Dueling network architectures for deep reinforcement learning.) (ICLR 最佳论文,伟大的想法 )


[48] Mnih, Volodymyr, et al. 用于深度强化学习的异步方法(Asynchronous methods for deep reinforcement learning.) (当前最先进的方法)


[49] Lillicrap, Timothy P., et al. 运用深度强化学习进行持续控制(Continuous control with deep reinforcement learning.) (DDPG) 


[50] Gu, Shixiang, et al. 带有模型加速的持续深层 Q-学习(Continuous Deep Q-Learning with Model-based Acceleration.)


[51] Schulman, John, et al. 信赖域策略优化(Trust region policy optimization.) (TRPO)


[52] Silver, David, et al. 使用深度神经网络和树搜索掌握围棋游戏(Mastering the game of Go with deep neural networks and tree search.) (阿尔法狗)


2.7 深度迁移学习/终身学习/尤其对于 RL


[53] Bengio, Yoshua. 表征无监督和迁移学习的深度学习(Deep Learning of Representations for Unsupervised and Transfer Learning.) (一个教程)


[54] Silver, Daniel L., Qiang Yang, and Lianghao Li. 终身机器学习系统:超越学习算法(Lifelong Machine Learning Systems: Beyond Learning Algorithms.) (一个关于终生学习的简要讨论)


[55] Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. 提取神经网络中的知识(Distilling the knowledge in a neural network.) (教父的工作) 


[56] Rusu, Andrei A., et al. 策略提取(Policy distillation.) (RL 领域)


[57] Parisotto, Emilio, Jimmy Lei Ba, and Ruslan Salakhutdinov. 演员模仿:深度多任务和迁移强化学习(Actor-mimic: Deep multitask and transfer reinforcement learning.) (RL 领域)


[58] Rusu, Andrei A., et al. 渐进神经网络(Progressive neural networks.)(杰出的工作,一项全新的工作)


2.8 一次性深度学习


[59] Lake, Brenden M., Ruslan Salakhutdinov, and Joshua B. Tenenbaum. 通过概率程序归纳达到人类水准的概念学习(Human-level concept learning through probabilistic program induction.)(不是深度学习,但是值得阅读)


[60] Koch, Gregory, Richard Zemel, and Ruslan Salakhutdinov. 用于一次图像识别的孪生神经网络(Siamese Neural Networks for One-shot Image Recognition.)


[61] Santoro, Adam, et al. 用记忆增强神经网络进行一次性学习(One-shot Learning with Memory-Augmented Neural Networks ) (一个一次性学习的基本步骤)


[62] Vinyals, Oriol, et al. 用于一次性学习的匹配网络(Matching Networks for One Shot Learning.)


[63] Hariharan, Bharath, and Ross Girshick. 少量视觉物体识别(Low-shot visual object recognition.)(走向大数据的一步)


3 应用


3.1 NLP(自然语言处理)


[1] Antoine Bordes, et al. 开放文本语义分析的词和意义表征的联合学习(Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing.)


[2] Mikolov, et al. 词和短语及其组合性的分布式表征(Distributed representations of words and phrases and their compositionality.) (word2vec)


[3] Sutskever, et al. 运用神经网络的序列到序列学习(Sequence to sequence learning with neural networks.)


[4] Ankit Kumar, et al. 问我一切:动态记忆网络用于自然语言处理(Ask Me Anything: Dynamic Memory Networks for Natural Language Processing.)


[5] Yoon Kim, et al. 角色意识的神经语言模型(Character-Aware Neural Language Models.)


[6] Jason Weston, et al. 走向人工智能-完成问题回答:一组前提玩具任务(Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks.) (bAbI 任务)


[7] Karl Moritz Hermann, et al. 教机器阅读和理解(Teaching Machines to Read and Comprehend.)(CNN/每日邮件完形风格问题)


[8] Alexis Conneau, et al. 非常深度卷曲网络用于自然语言处理(Very Deep Convolutional Networks for Natural Language Processing.) (在文本分类中当前最好的)


[9] Armand Joulin, et al. 诡计包用于有效文本分类(Bag of Tricks for Efficient Text Classification.)(比最好的差一点,但快很多)


3.2 目标检测


[1] Szegedy, Christian, Alexander Toshev, and Dumitru Erhan. 深度神经网路用于目标检测(Deep neural networks for object detection.)


[2] Girshick, Ross, et al. 富特征层级用于精确目标检测和语义分割(Rich feature hierarchies for accurate object detection and semantic segmentation.)(RCNN)


[3] He, Kaiming, et al. 深度卷曲网络的空间金字塔池用于视觉识别(Spatial pyramid pooling in deep convolutional networks for visual recognition.) (SPPNet)


[4] Girshick, Ross. 快速的循环卷曲神经网络(Fast r-cnn.)


[5] Ren, Shaoqing, et al. 更快的循环卷曲神经网络:通过区域建议网络趋向实时目标检测(Faster R-CNN: Towards real-time object detection with region proposal networks.)


[6] Redmon, Joseph, et al. 你只看到一次:统一实时的目标检测(You only look once: Unified, real-time object detection.) (YOLO, 杰出的工作,真的很实用)


[7] Liu, Wei, et al. SSD:一次性多盒探测器(SSD: Single Shot MultiBox Detector.)


3.3 视觉跟踪


[1] Wang, Naiyan, and Dit-Yan Yeung. 学习视觉跟踪用的一种深度压缩图象表示(Learning a deep compact image representation for visual tracking.) (第一篇使用深度学习进行视觉跟踪的论文,DLT 跟踪器)


[2] Wang, Naiyan, et al. 为稳定的视觉跟踪传输丰富特征层次(Transferring rich feature hierarchies for robust visual tracking.)(SO-DLT)


[3] Wang, Lijun, et al. 用全卷积网络进行视觉跟踪(Visual tracking with fully convolutional networks.) (FCNT)


[4] Held, David, Sebastian Thrun, and Silvio Savarese. 用深度回归网络以 100FPS 速度跟踪(Learning to Track at 100 FPS with Deep Regression Networks.) (GOTURN, 作为一个深度神经网络,其速度非常快,但是相较于非深度学习方法还是慢了很多)


[5] Bertinetto, Luca, et al. 对象跟踪的全卷积 Siamese 网络(Fully-Convolutional Siamese Networks for Object Tracking.) (SiameseFC, 实时对象追踪的最先进技术)


[6] Martin Danelljan, Andreas Robinson, Fahad Khan, Michael Felsberg. 超越相关滤波器:学习连续卷积算子的视觉追踪(Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking.)(C-COT)


[7] Nam, Hyeonseob, Mooyeol Baek, and Bohyung Han. 在视觉跟踪的树结构中传递卷积神经网络与建模(Modeling and Propagating CNNs in a Tree Structure for Visual Tracking.)(VOT2016 Winner,TCNN)


3.4 图像说明


[1] Farhadi,Ali,etal. 每幅图都讲述了一个故事:从图像中生成句子(Every picture tells a story: Generating sentences from images.)


[2] Kulkarni, Girish, et al. 儿语:理解并生成图像的描述(talk: Understanding and generating image descriptions.)


[3] Vinyals, Oriol, et al. 展示与表达:一个神经图像字幕生成器(Show and tell: A neural image caption generator)


[4] Donahue, Jeff, et al. 视觉认知和描述的长期递归卷积网络(Long-term recurrent convolutional networks for visual recognition and description)


[5] Karpathy, Andrej, and Li Fei-Fei. 产生图像描述的深层视觉语义对齐(Deep visual-semantic alignments for generating image descriptions)


[6] Karpathy, Andrej, Armand Joulin, and Fei Fei F. Li. 双向图像句映射的深片段嵌入(Deep fragment embeddings for bidirectional image sentence mapping)


[7] Fang, Hao, et al. 从字幕到视觉概念,从视觉概念到字幕(From captions to visual concepts and back)


[8] Chen, Xinlei, and C. Lawrence Zitnick. 图像字幕生成的递归视觉表征学习「Learning a recurrent visual representation for image caption generation


[9] Mao, Junhua, et al. 使用多模型递归神经网络(m-rnn)的深度字幕生成(Deep captioning with multimodal recurrent neural networks (m-rnn).)


[10] Xu, Kelvin, et al. 展示、参与与表达:视觉注意的神经图像字幕生成(Show, attend and tell: Neural image caption generation with visual attention.)


3.5 机器翻译


一些里程碑式的论文在 RNN \序列到序列的主题分类下被列举。


[1] Luong, Minh-Thang, et al. 神经机器翻译中生僻词问题的处理(Addressing the rare word problem in neural machine translation.)


[2] Sennrich, et al. 带有子词单元的生僻字神经机器翻译(Neural Machine Translation of Rare Words with Subword Units)


[3] Luong, Minh-Thang, Hieu Pham, and Christopher D. Manning. 基于注意力的神经机器翻译的有效途径(Effective approaches to attention-based neural machine translation.)


[4] Chung, et al. 一个机器翻译无显式分割的字符级解码器(A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation)


[5] Lee, et al. 无显式分割的全字符级神经机器翻译(Fully Character-Level Neural Machine Translation without Explicit Segmentation)


[6] Wu, Schuster, Chen, Le, et al. 谷歌的神经机器翻译系统:弥合人与机器翻译的鸿沟(Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation)


3.6 机器人


[1] Koutník, Jan, et al. 发展用于视觉强化学习的大规模神经网络(Evolving large-scale neural networks for vision-based reinforcement learning.)


[2] Levine, Sergey, et al. 深度视觉眼肌运动策略的端到端训练(End-to-end training of deep visuomotor policies.)


[3] Pinto, Lerrel, and Abhinav Gupta. 超大尺度自我监督:从 5 万次尝试和 700 机器人小时中学习抓取(Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours.)


[4] Levine, Sergey, et al. 学习手眼协作用于机器人掌握深度学习和大数据搜集(Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection.)


[5] Zhu, Yuke, et al. 使用深度强化学习视觉导航目标驱动的室内场景(Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning.)


[6] Yahya, Ali, et al. 使用分布式异步引导策略搜索进行集体机器人增强学习(Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search.)


[7] Gu, Shixiang, et al. 深度强化学习用于机器操控(Deep Reinforcement Learning for Robotic Manipulation.)


[8] A Rusu, M Vecerik, Thomas Rothörl, N Heess, R Pascanu, R Hadsell. 模拟实机机器人使用过程网从像素中学习(Sim-to-Real Robot Learning from Pixels with Progressive Nets.)


[9] Mirowski, Piotr, et al. 学习在复杂环境中导航(Learning to navigate in complex environments.)


3.7 艺术


[1] Mordvintsev, Alexander; Olah, Christopher; Tyka, Mike (2015). 初始主义:神经网络的更深层(Inceptionism: Going Deeper into Neural Networks)(谷歌 Deep Dream) 


[2] Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. 一个艺术风格的神经算法(A neural algorithm of artistic style.) (杰出的工作,目前最成功的算法) 


[3] Zhu, Jun-Yan, et al. 自然图像流形上的生成视觉操纵(Generative Visual Manipulation on the Natural Image Manifold.)


[4] Champandard, Alex J. Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks. (神经涂鸦) 


[5] Zhang, Richard, Phillip Isola, and Alexei A. Efros. 多彩的图像彩色化(Colorful Image Colorization.)


[6] Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. 实时风格迁移和超分辨率的感知损失(Perceptual losses for real-time style transfer and super-resolution.)


[7] Vincent Dumoulin, Jonathon Shlens and Manjunath Kudlur. 一个艺术风格的学习表征(A learned representation for artistic style.)


[8] Gatys, Leon and Ecker, et al. 神经风格迁移中的控制感知因子(Controlling Perceptual Factors in Neural Style Transfer.) (控制空间定位、色彩信息和全空间尺度方面的风格迁移)


[9] Ulyanov, Dmitry and Lebedev, Vadim, et al. 纹理网络:纹理和风格化图像的前馈合成(Texture Networks: Feed-forward Synthesis of Textures and Stylized Images.) (纹理生成和风格迁移)


3.8 对象分割


[1] J. Long, E. Shelhamer, and T. Darrell, 用于语义分割的全卷积网络(Fully convolutional networks for semantic segmentation)


[2] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. 具有深度卷积网络和全连接的条件随机场的语义图像分割(Semantic image segmentation with deep convolutional nets and fully connected crfs)


[3] Pinheiro, P.O., Collobert, R., Dollar, P. 学习如何分割候选对象(Learning to segment object candidates)


[4] Dai, J., He, K., Sun, J. 基于多任务网络级联的实例感知语义分割(Instance-aware semantic segmentation via multi-task network cascades)


[5] Dai, J., He, K., Sun, J. 实例敏感的全卷积网络(Instance-sensitive Fully Convolutional Networks)

入门深度学习论文理论
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