split deep_learning.md into serveral parts

dl_cnn.md

@@ -0,0 +1,54 @@
+## Convolutional Nerual Netowrks
+---
+
+##### ImageNet Records  
+- 2016 arXiv [Identity Mappings in Deep Residual Networks](http://arxiv.org/pdf/1603.05027v1.pdf)    
+- 2016 arXiv [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](http://arxiv.org/abs/1602.07261) (Inception V4)  
+- 2015 arXiv [Deep Residual Learning for Image Recognition](http://arxiv.org/abs/1512.03385) (ResNet)     
+- 2015 arXiv [Rethinking the Inception Architecture for Computer Vision](http://arxiv.org/abs/1512.00567) (Inception V3)  
+- 2015 ICML [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](http://jmlr.org/proceedings/papers/v37/ioffe15.pdf) (Inception V2)  
+- 2015 ICCV [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](http://research.microsoft.com/en-us/um/people/kahe/publications/iccv15imgnet.pdf) (PReLU)  
+- 2015 ICLR [Very Deep Convolutional Networks For Large-scale Image Recognition](http://arxiv.org/abs/1409.1556) (VGG)  
+- 2015 CVPR [Going Deeper with Convolutions](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43022.pdf) (GoogleNet/Inception V1)   
+- 2012 NIPS [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) (AlexNet)  
+
+##### Arichitecture Design
+
+- 2016 arXiv [Benefits of depth in neural networks](http://arxiv.org/abs/1602.04485)  
+- 2016 AAAI [On the Depth of Deep Neural Networks: A Theoretical View](http://arxiv.org/abs/1506.05232)  
+- 2016 arXiv [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size](http://arxiv.org/abs/1602.07360)  
+- 2015 CVPR [Convolutional Neural Networks at Constrained Time Cost](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/He_Convolutional_Neural_Networks_2015_CVPR_paper.pdf)  
+- 2015 ICLR [FitNets: Hints for Thin Deep Nets](http://arxiv.org/pdf/1412.6550v4.pdf)  
+- 2014 NIPS [Do Deep Nets Really Need to be Deep?](http://papers.nips.cc/paper/5484-do-deep-nets-really-need-to-be-deep.pdf)  
+- 2014 ICLRW [Understanding Deep Architectures using a Recursive Convolutional Network](http://arxiv.org/abs/1312.1847)  
+- 2014 ECCV [Visualizing and Understanding Convolutional Networks](https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf)  
+- 2009 ICCV [What is the Best Multi-Stage Architecture for Object Recognition?](http://yann.lecun.com/exdb/publis/pdf/jarrett-iccv-09.pdf)   
+- 1994 T-NN [SVD-NET: An Algorithm that Automatically Selects Network Structure](http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=286929)  
+
+##### Network Binarization
+- 2016 arXiv [XNOR-Net: ImageNet Classification Using Binary
+Convolutional Neural Networks](http://arxiv.org/pdf/1603.05279v1.pdf)  
+- 2016 arXiv [Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1](http://arxiv.org/abs/1602.02830)  
+- 2015 NIPS [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://papers.nips.cc/paper/5647-binaryconnect-training-deep-neural-networks-with-binary-weights-during-propagations.pdf)  
+
+##### Model Compression / Parameter Pruning
+- 2016 ICLR [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](http://arxiv.org/abs/1510.00149)  
+- 2016 ICLR [Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications](http://arxiv.org/abs/1511.06530)
+- 2015 arXiv [Training CNNs with Low-Rank Filters for Efficient Image Classification](http://arxiv.org/abs/1511.06744)  
+- 2015 arXiv [Structured Pruning of Deep Convolutional Neural Networks](http://arxiv.org/abs/1512.08571)  
+- 2015 arXiv [Data-free parameter pruning for Deep Neural Networks](http://arxiv.org/abs/1507.06149)  
+- 2015 ICCV [An exploration of parameter redundancy in deep networks with circulant projections](http://felixyu.org/pdf/ICCV15_circulant.pdf)  
+- 2015 ICML [Compressing Neural Networks with the Hashing Trick](http://jmlr.org/proceedings/papers/v37/chenc15.pdf)  
+- 2015 NIPS [Learning both Weights and Connections for Efficient Neural Networks](http://arxiv.org/abs/1506.02626)  
+- 2014 arXiv [Compressing deep convolutional networks
+using vector quantization](http://arxiv.org/abs/1412.6115)  
+- 2014 NIPSW [Distilling the Knowledge in a Neural Network](https://fb56552f-a-62cb3a1a-s-sites.googlegroups.com/site/deeplearningworkshopnips- 2014/65.pdf?attachauth=ANoY7cr8J-eqASFdYZeOQK8d9aGCtxzQpaVNCcjKgt1THV7e9FKNuTlrH4QCPmgMg2jynAz3ehjOU_2q9SMsnBYZq3_Jlxf1NnWcBejaVZi4vNHZ41H2DK8R-MJsk3MqfMDXOfEPxhAAOwUBH7oE-EtEKDoYa-16eqZ5djaoT4VXdir383rikNv6YF68dhm84kw04VCzH5XpA_8ucgW3iBr77bkjaYvNvC6YsUuC3PyVEPIusOZaM94%3D&attredirects=0)   
+- 1989 NIPS [Optimal Brain Damage](http://yann.lecun.com/exdb/publis/pdf/lecun-90b.pdf)  
+
+<!--##### Other
+- 2013 PAMI [Invariant Scattering Convolution Networks](http://www.di.ens.fr/data/publications/papers/pami-final.pdf)  -->
+
+#### Applications  
+- 2015 CVPR [FaceNet: A Unified Embedding for Face Recognition and Clustering](http://arxiv.org/abs/1503.03832)  
+- 2012 CVPR [Towards Good Practice in Large-Scale Learning for Image Classification](http://hal.inria.fr/docs/00/69/00/14/PDF/cvpr2012.pdf)  
+- 2012 ICML [Building High-level Features Using Large Scale Unsupervised Learning](http://static.googleusercontent.com/media/research.google.com/en/us/archive/unsupervised_icml2012.pdf)  

dl_opt.md

@@ -0,0 +1,21 @@
+## Optimization 
+---
+
+- 2016 ICLR [Data-Dependent Path Normalization in Neural Networks](http://arxiv.org/pdf/1511.06747v4.pdf)  
+- 2016 Blog [An overview of gradient descent optimization algorithms](http://sebastianruder.com/optimizing-gradient-descent/index.html)  
+- 2015 arXiv [Adding Gradient Noise Improves Learning for Very Deep Networks](http://arxiv.org/abs/1511.06807)      
+- 2015 DL Summer School [Non-Smooth, Non-Finite, and Non-Convex Optimization](http://www.iro.umontreal.ca/~memisevr/dlss- 2015/- 2015_DLSS_NonSmoothNonFiniteNonConvex.pdf)  
+- 2015 NIPS [Training Very Deep Networks](http://papers.nips.cc/paper/5850-training-very-deep-networks.pdf)  
+- 2015 NIPS [Deep learning with Elastic Averaging SGD](https://www.cs.nyu.edu/~zsx/nips- 2015.pdf) (EASGD)  
+- 2015 CVPR [Convolutional Neural Networks at Constrained Time Cost](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/He_Convolutional_Neural_Networks_2015_CVPR_paper.pdf)  
+- 2015 ICMLW [Highway Networks](http://arxiv.org/pdf/1505.00387v2.pdf)  
+- 2015 ICLR [Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging](http://arxiv.org/pdf/1409.1556v6.pdf)  
+- 2015 ICLR [Adam: A Method for Stochastic Optimization](http://arxiv.org/abs/1412.6980) (Adam)   
+- 2015 AISTATS [Deeply-Supervised Nets](http://jmlr.org/proceedings/papers/v38/lee15a.pdf)  
+- 2014 JMLR [Dropout: A Simple Way to Prevent Neural Networks from
+Overfitting](https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf) (Dropout)    
+- 2014 NIPS [Identifying and attacking the saddle point problem in high-dimensional non-convex optimization](http://papers.nips.cc/paper/5486-identifying-and-attacking-the-saddle-point-problem-in-high-dimensional-non-convex-optimization.pdf)  
+- 2014 OSLW [On the Computational Complexity of Deep Learning](http://lear.inrialpes.fr/workshop/osl- 2015/slides/osl- 2015_shalev_shwartz.pdf)  
+- 2013 ICML [On the importance of initialization and momentum in deep learning](http://www.cs.utoronto.ca/~ilya/pubs/- 2013/1051_2.pdf)  
+- 2011 ICML [On optimization methods for deep learning](http://ai.stanford.edu/~quocle/LeNgiCoaLahProNg11.pdf)  
+- 2010 AISTATS [Understanding the difficulty of training deep feedforward neural networks](http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf)