页数	论文名
9	A Few Useful Things to Know about Machine Learning
5	ADVANCES IN OPTIMIZING RECURRENT NETWORKS
39	Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
9	An Empirical Exploration of Recurrent Network Architectures
8	Backpropagation, Intuitions
11	Batch Normalization-Accelerating Deep Network Training by Reducing Internal Covariate Shift
9	Beyond Short Snippets-Deep Networks for Video Classification
8	CNN Features off-the-shelf- an Astounding Baseline for Recognition
14	Convolutional Neural Networks- Architectures, Convolution, Pooling Layers
10	DeCAF-A Deep Convolutional Activation Feature
8	Deep Inside Convolutional Networks-Visualising Image Classification Models and Saliency Maps
6	Deep Learning using Linear Support Vector Machines
12	Deep Residual Learning for Image Recognition
8	DeepFace-Closing the Gap to Human-Level Performance in Face Verification
8	Deformable Part Models are Convolutional Neural Networks
11	Delving Deep into Rectifiers
11	Delving Deeper into Convolutional Networks for Learning Video Representations
9	Distributed Representations of Words and Phrases
9	Do Convnets Learn Correspondence
9	Dropout Training as Adaptive Regularization
30	Dropout- A Simple Way to Prevent Neural Networks from Overfitting
11	EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES
44	Efficient BackProp
9	Fast R-CNN
13	Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods
14	Faster R-CNN- Towards Real-Time Object Detection with Region Proposal Networks
12	Going Deeper with Convolutions 
4	Hessian matrix - Wikipedia, the free encyclopedia
14	How transferable are features in deep neural networks
10	Image Classification- Data-driven Approach, k-Nearest Neighbor, train:val:test splits 
9	ImageNet Classification with Deep Convolutional
43	ImageNet Large Scale Visual Recognition Challenge
10	Intriguing properties of neural networks
18	LSTM- A Search Space Odyssey
11	Large Scale Distributed Deep Networks
8	Large-scale Video Classification with Convolutional Neural Networks
16	Learning Spatiotemporal Features with 3D Convolutional Networks
12	Linear classification- Support Vector Machine, Softmax
13	Long-term Recurrent Convolutional Networks for Visual Recognition and Description
9	Maxout Networks
8	Neural Networks Part 1- Setting up the Architecture
10	Neural Networks Part 2- Setting up the Data and the Loss
12	Neural Networks Part 3- Learning and Evaluation
12	On the difficulty of training Recurrent Neural Networks
9	Optimization- Stochastic Gradient Descent
16	OverFeat-Integrated Recognition, Localization and Detection using Convolutional Networks
33	Practical Recommendations for Gradient-Based Training of Deep Arch
25	Random Search for Hyper-Parameter Optimization
21	Rich feature hierarchies for accurate object detection and semantic segmentation
14	STRIVING FOR SIMPLICITY- THE ALL CONVOLUTIONAL NET
14	Selective Search for Object Recognition
22	Show, Attend and Tell- Neural Image Caption Generation with Visual Attention
16	Stochastic Gradient Descent Tricks
2	Transfer Learning
11	Two-Stream Convolutional Networks for Action Recognition in Videos
9	Understanding Deep Image Representations by Inverting Them
5	Understanding and Visualizing Convolutional Neural Networks
8	Understanding the difficulty of training deep feedforward neural networks
13	Unit Tests for Stochastic Optimization
14	Very Deep Convolutional Networks for Large-Scale Image Recognition
11	Visualizing and Understanding Convolutional Networks
6	What I learned from competing against a ConvNet on ImageNet
16	What makes for effective detection proposals
7	video process