一、書籍
Deep learning (2015)
作者：Bengio
下載地址：http://www.deeplearningbook.org/
二、理論
1.在神經網絡中提取知識
Distilling the knowledge in a neural network
作者：G. Hinton et al.
2.深度神經網絡很易受騙：高信度預測無法識別的圖片
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
作者：A. Nguyen et al.
3.深度神經網絡特徵的可遷移性如何？
How transferable are features in deep neural networks? (2014),
作者：J. Yosinski et al.
4.深挖卷積網絡的各個細節
Return of the Devil in the Details: Delving Deep into Convolutional Nets (2014)
作者：K. Chatfield et al.
5.爲什麼無監督預訓練對深度學習有幫助？
Why does unsupervised pre-training help deep learning (2010)
作者：D. Erhan et al. (Bengio)
6.理解訓練深度前饋神經網絡的難點
Understanding the difficulty of training deep feedforward neural networks (2010)
作者：X. Glorot and Y. Bengio
三、優化/網絡結構
簡介：本部分從文獻7到文獻14爲神經網絡優化的一些方法，尤其是文獻7的批歸一化更是在業界產生巨大的影響；文獻15到文獻22爲網絡結構的變化，包括全卷積神經網絡等。這些參考文獻都是非常具有參考價值的乾貨！
7.Batch Normalization 算法：通過減少內部協變量轉化加速深度網絡的訓練（推薦）
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (2015)
作者：S. Loffe and C. Szegedy (Google)
8.Dropout：一個預防神經網絡過擬合的簡單方式
Dropout: A simple way to prevent neural networks from overfitting (2014)
作者：N. Srivastava et al. (Hinton)
9.Adam：一個隨機優化的方法
Adam: A method for stochastic optimization (2014)
作者：D. Kingma and J. Ba
10.論深度學習領域初始化和動量的重要性
On the importance of initialization and momentum in deep learning (2013)
作者：I. Sutskever et al. (Hinton)
11.使用 Dropconnect 的神經網絡正則化
Regularization of neural networks using dropconnect (2013)
作者：L. Wan et al. (LeCun)
12.超參數最優化的隨機搜索
Random search for hyper-parameter optimization (2012)
作者：J. Bergstra and Y. Bengio
13.圖像識別中的深度殘差學習
Deep residual learning for image recognition (2016)
作者：K. He et al. (Microsoft)
14.用於物體精準檢測和分割的基於區域的卷積網絡
Region-based convolutional networks for accurate object detection and segmentation (2016)
作者：R. Girshick et al.(Microsoft)
15.更深的卷積網絡
Going deeper with convolutions (2015)
作者：C. Szegedy et al. (Google)
16.快速 R-CNN 網絡
Fast R-CNN (2015)
作者： R. Girshick (Microsoft)
16.更快速的 R-CNN 網絡：使用區域網絡的實時物體檢測
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015)
作者： S. Ren et al.
17.用於語義分割的全卷積神經網絡
Fully convolutional networks for semantic segmentation (2015)
作者：J. Long et al.
18.大規模圖像識別的深度卷積網絡
Very deep convolutional networks for large-scale image recognition (2014)
作者：K. Simonyan and A. Zisserman
19.OverFeat：使用卷積網絡融合識別、本地化和檢測
OverFeat: Integrated recognition, localization and detection using convolutional networks (2014)
作者：P. Sermanet et al.(LeCun)
20.可視化以及理解卷積網絡
Visualizing and understanding convolutional networks (2014)
作者：M. Zeiler and R. Fergus
21.Maxout 網絡
Maxout networks (2013)
作者：I. Goodfellow et al. (Bengio)
22.Network In Network 深度網絡架構
Network in network (2013)
作者：M. Lin et al.
四、圖像
１.使用卷積神經網絡在自然環境下閱讀文本
Reading text in the wild with convolutional neural networks (2016)
作者：M. Jaderberg et al. (DeepMind)
２.Imagenet 大規模視覺識別挑戰賽
Imagenet large scale visual recognition challenge (2015)
作者：O. Russakovsky et al.
3.DRAW：一個用於圖像生成的循環神經網絡
DRAW: A recurrent neural network for image generation (2015)
作者：K. Gregor et al.
4.對精確的物體檢測和語義切割更爲豐富的特徵分層
Rich feature hierarchies for accurate object detection and semantic segmentation (2014)
作者： R. Girshick et al.
5.使用卷積神經網絡學習和遷移中層圖像表徵
Learning and transferring mid-Level image representations using convolutional neural networks (2014)
作者：M. Oquab et al.
6.DeepFace：在面部驗證任務中接近人類表現
DeepFace: Closing the Gap to Human-Level Performance in Face Verification (2014)
作者：Y. Taigman et al. (Facebook)
五、視頻 / 人類行爲
1.利用卷積神經網絡進行大規模視頻分類（2014）
Large-scale video classification with convolutional neural networks (2014)
作者：A. Karpathy et al. (FeiFei)
2.DeepPose：利用深度神經網絡評估人類姿勢
DeepPose: Human pose estimation via deep neural networks (2014)
作者：A. Toshev and C. Szegedy (Google)
3.用於視頻中動作識別的雙流卷積網絡
Two-stream convolutional networks for action recognition in videos (2014)
作者：K. Simonyan et al.
4.用於人類動作識別的 3D 卷積神經網絡（這篇文章針對連續視頻幀進行處理，是個不錯的）
3D convolutional neural networks for human action recognition (2013)
作者：S. Ji et al.
5.帶有改進軌跡的動作識別
Action recognition with improved trajectories (2013)
作者：H. Wang and C. Schmid
6.用獨立子空間分析，學習用於動作識別的等級恆定的時空特徵
Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis (2011)
作者：Q. Le et al
六、自然語言處理
1.用 RNN 編碼——解碼器學習短語表徵，實現統計機器翻譯
Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014)
作者：K. Cho et al.(Bengio)
2.一個爲句子建模的卷積神經網絡
A convolutional neural network for modelling sentences (2014)
作者：N. Kalchbrenner et al.
3.用於句子分類的卷積神經網絡
Convolutional neural networks for sentence classification (2014)
作者：Y. Kim
4.斯坦福 coreNLP 自然語言處理工具
The stanford coreNLP natural language processing toolkit (2014)
作者：C. Manning et al.
5.基於情感樹庫應用於情感組合研究的遞歸深度網絡模型
Recursive deep models for semantic compositionality over a sentiment treebank (2013)
作者：R. Socher et al.
6.基於語言模型的循環神經網絡
Recurrent neural network based language model (2010)
作者：T. Mikolov et al.
7.自動語音識別：一種深度學習的方法
Automatic Speech Recognition - A Deep Learning Approach (Book, 2015)
作者：D. Yu and L. Deng (Microsoft)
8.使用深度循環網絡進行語音識別
Speech recognition with deep recurrent neural networks (2013)
作者：A. Graves (Hinton)
9.基於上下文預訓練的深度神經網絡在大規模詞表語音識別中的應用
Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition (2012)
作者：G. Dahl et al.
10.使用深度信念網絡進行聲學建模
Acoustic modeling using deep belief networks (2012)
作者：A. Mohamed et al. (Hinton)
七、無監督學習
1.自編碼變量貝葉斯
Auto-Encoding Variational Bayes (2013)
作者：D. Kingma and M. Welling
2.用大規模無監督學習搭建高水平特徵
Building high-level features using large scale unsupervised learning (2013)
作者：Q. Le et al.
3.無監督特徵學習中單層網絡分析
An analysis of single-layer networks in unsupervised feature learning (2011)
作者：A. Coates et al.
4.堆棧降噪解碼器：在本地降噪標準的深度網絡中學習有用的表徵
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion (2010)
作者：P. Vincent et al. (Bengio)
5.訓練受限波茲曼機的實踐指南
A practical guide to training restricted boltzmann machines (2010)
作者：G. Hinton
八、開源架構
1.TensorFlow：異構分佈式系統上的大規模機器學習
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (2016)
作者：M. Abadi et al. (Google)
2.Theano：一個針對快速計算數學表達公式的Python框架
Theano: A Python framework for fast computation of mathematical expressions
作者：R. Al-Rfou et al. (Bengio)
3.MatConvNet: 針對matlab 的卷積神經網絡
MatConvNet: Convolutional neural networks for matlab (2015)
作者：A. Vedaldi and K. Lenc
4.Caffe：快速特徵嵌入的卷積結構
Caffe: Convolutional architecture for fast feature embedding (2014)
作者： Y. Jia et al.
九、2016最新論文
1.對立學習推論
Adversarially Learned Inference (2016)
作者：V. Dumoulin et al.
2.理解卷積神經網絡
Understanding Convolutional Neural Networks (2016)
作者：J. Koushik
3.SqueezeNet 模型：達到 AlexNet 水平的準確率，卻使用縮減 50 倍的參數以及< 1MB 的模型大小
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size (2016)
作者：F. Iandola et al.
4.學習搭建問答神經網絡
Learning to Compose Neural Networks for Question Answering (2016)
作者：J. Andreas et al.
5.用深度學習和大規模數據蒐集，學習眼手協調的機器人抓取
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection (2016)(Google)
作者：S. Levine et al.
6.將人隔離在外：貝葉斯優化算法回顧
Taking the human out of the loop: A review of bayesian optimization (2016)
作者：B. Shahriari et al.
7.Eie：壓縮神經網絡的高效推理引擎
Eie: Efficient inference engine on compressed deep neural network (2016)
作者：S. Han et al.
8.循環神經網絡的自適性計算時間
Adaptive Computation Time for Recurrent Neural Networks (2016)
作者：A. Graves
9.像素循環神經網絡
Pixel Recurrent Neural Networks (2016)
作者：A. van den Oord et al. (DeepMind)
10.LSTM：一場搜索空間的奧德賽之旅
LSTM: A search space odyssey (2016)
作者：K. Greff et al.
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作者：深度學習思考者
來源：CSDN
原文：https://blog.csdn.net/u010402786/article/details/51682917
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