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YuanSiping · Jan 8, 2018 · 1392555 · 1392555
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diff --git a/database-subject.csv b/database-subject.csv
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+id,label,images,subject
+1,A,82,AlexNet��ImageNet Classification with Deep Convolutional Neural Networks ImageNet
+2,AC,58,A computational intelligence technique for the effective diagnosis of diabetic patients using principal component analysis (PCA) and modified fuzzy SLIQ decision tree approach
+3,AM,68,A cost sensitive decision tree algorithm based on weighted class distribution with batch deleting attribute mechanism
+4,AN,22,A Novel Visualization Method of Power Transmission Lines
+5,AP,42,"Application and comparison of RNN, RBFNN and MNLR approaches on prediction of flotation column performance"
+6,AR,87,Association rule mining with mostly associated sequential patterns
+7,AS,49,A survey on deep learning-based fine-grained object classification and semantic segmentation
+8,BC,24,BinaryConnect Training Deep Neural Networks with binary weights during propagations
+9,BT,65,Binarized Neural Networks Training Neural Networks withWeights and Activations Constrained to +1 or -1
+10,C,92,Cambricon��An Instruction Set Architecture for Neural Networks
+11,CN,66,Convolutional Neural Networks using Logarithmic Data Representation
+12,CP,81,Channel Pruning for Accelerating Very Deep Neural Networks
+13,CX,86,Cambricon-X��An Accelerator for Sparse Neural Networks
+14,D,50,Distance and similarity measures between hesitant fuzzy sets and their application in pattern recognition
+15,DC,73,"Deep Compression��Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding"
+16,DDN,90,DaDianNao��A Machine-Learning Supercomputer
+17,DE,84,Deep learning with low precision by half-wave gaussian quantization
+18,DI,112,Distilling the Knowledge in a Neural Network
+19,DL,89,Deep learning (nature 14539)
+20,DM,91,Deep Model Compression��Distilling Knowledge from Noisy Teachers
+21,DN,96,DianNao��A Small-Footprint High-Throughput Accelerator for Ubiquitous Machine-Learning
+22,DNS,56,Dynamic Network Surgery for Efficient DNNs
+23,DO,80,"Design of Efficient Convolutional Layers using Single Intra-channel Convolution,Topological Subdivisioning and Spatial ""Bottleneck"" Structure"
+24,DR,80,DoReFa-Net��Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
+25,DS,77,DSD:Dense-Sparse-Dense Training for Deep Neural Networks
+26,E,43,Evaluating the capacity planning of industrial self-generation in penetration of renewable energy
+27,EB,67,Economic batch sizing and scheduling on parallel machines under time-of-use electricity pricing
+28,EI,112,EIE��Efficient Inference Engine on Compressed Deep Neural Network
+29,EL,23,An economic and low-carbon day-ahead Pareto-optimal scheduling for wind farm integrated power systems with demand response
+30,EN,58,Energy Storage Modeling for Distribution Planning
+31,EQ,75,Effective Quantization Methods for Recurrent Neural Networks
+32,EX,83,Exploiting linear structure within convolutional networks for ef?cient evaluation
+33,F,113,Feature selection methods for big data bioinformatics A survey from the search perspective
+34,FM,43,Face Model Compression by Distilling Knowledge from Neurons
+35,G,90,Geometric-Similarity Retrieval in Large Image Bases
+36,GC,38,Green Computing Evaluation Process
+37,GE,60,Genealogy of the��Grandmother Cell��
+38,GO,66,GoogLeNet��Going deeper with convolutions
+39,GR,71,Gender recognition and biometric identification using a large dataset of hand images
+40,H,44,Hardware-oriented Approximation of Convolutional Neural Networks
+41,HA,96,HashNet��Deep Learning to Hash by Continuation
+42,I,22,Implementation of High Accuracy-based Image Transformation Module in Cloud Computing
+43,IM,49,Improving the speed of neural networks on CPUs
+44,IN,46,Incremental Network Quantization��Towards Lossless CNNs with Low-Precision Weights
+45,IP,42,An Improved Particle Swarm Optimization for Economic Dispatch with Carbon Tax
+46,IV,65,"Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning"
+47,L,106,LLNet��A deep auto encoder approach to natural low-light image enhancement
+48,LB,51,Learning both Weights and Connections for Ef?cient Neural Networks
+49,LS,52,Large Scale Distributed Deep Networks
+50,M,42,MPtostream an OpenMP compiler for CPU-GPU heterogeneous parallel systems
+51,ME,70,MobileNets��Efficient Convolutional Neural Networks for Mobile Vision Applications
+52,N,17,Carbon Emissions Modeling of China Using Neural Network
+53,NN,57,Network In Network
+54,O,146,On predicting learning styles in conversational intelligent tutoring systems using fuzzy decision trees
+55,OD,64,Object detectors emerge in deep scene CNNs
+56,OP,42,Optimizing Performance of Recurrent Neural Networks on GPUs
+57,OT,43,On the Compression of Recurrent Neural Networks with an Application to LVCSR acoustic modeling for Embedded Speech Recognition
+58,P,80,Pattern Recognition in Latin America in the��Big Data��Era
+59,PE,73,PerforatedCNNs��Acceleration through Elimination of Redundant Convolutions
+60,PF,67,Pruning Filters for Efficient ConvNets
+61,PI,114,Potential improvement of classifier accuracy by using fuzzy measures
+62,PL,86,Perceptual Losses for Real-Time Style Transfer and Super-Resolution
+63,PM,51,Probabilistic Non-Local Means
+64,PN,35,Production Strategy of Carbon Sensitive Products under Low-Carbon Policies
+65,PO,22,Research of Power Generation Right Transaction Scheduling Model Considering Carbon Emission Constraint Blocking
+66,PR,108,Palmprint recognition with Local Micro-structure Tetra Pattern
+67,PV,39,PVANet��Deep but Lightweight Neural Networks for Real-time Object Detection
+68,Q,95,Quantized Convolutional Neural Networks for Mobile Devices
+69,QN,121,Quantized neural networks��Training neural networks with low precision weights and activations
+70,R,82,Natural image statistics and neural representation
+71,RA,71,Refining Architectures of Deep Convolutional Neural Networks
+72,RD,41,Reshaping deep neural network for fast decoding by node-pruning
+73,RF,156,Rich feature hierarchies for accurate object detection and semantic segmentation Tech report
+74,RO,43,Restructuring of Deep Neural Network Acoustic Models with Singular Value Decomposition
+75,RS,123,Deep Residual Learning for Image Recognition
+76,S,39,Skew Correction and Line Extraction in Binarized Printed Text Images
+77,SA,97,SqueezeNet��AlexNet-level accuracy with 50x fewer parameters and <1MB model size
+78,SC,81,Scalable and modularized RTL compilation of Convolutional Neural Networks onto FPGA
+79,SDN,87,ShiDianNao��Shifting Vision Processing Closer to the Sensor
+80,SG,124,Scalable and Sustainable Deep Learning via Randomized Hashing
+81,SH,49,ShuffleNet��An Extremely Efficient Convolutional Neural Network for Mobile Devices
+82,SP,71,Sparsifying Neural Network Connections for Face Recognition
+83,SS,125,Semisupervised Subspace-Based DNA Encoding and Matching Classifier for Hyperspectral Remote Sensing Imagery
+84,T,24,Ternary weight networks
+85,TR,63,Text Recognition for Information Retrieval in Images of Printed Circuit Boards
+86,TS,67,Outrageously Large Neural Networks��The Sparsely-Gated Mixture-of-Experts Layer
+87,TT,66,Trained Ternary Quantization
+88,TW,26,The ways of streamlining digital image processing algorithms used for detection of lines in transport scenes video recording
+89,U,161,"Urban Computing�� Concepts, Methodologies, and Applications"
+90,V,59,Return of the Devil in the Details: Delving Deep into Convolutional Nets
+91,X,36,XNOR-Net��ImageNet Classification Using Binary Convolutional Neural Networks
+92,XD,60,Xception��Deep Learning with Depthwise Separable Convolutions
+93,һ,50,һ����ӱ���Զ�ͼ���ע����
+94,��,126,��������ʮ�꣺�ع���չ��
+95,��,107,�����������ѧϰ����
+96,��,48,���Ի�ͼ���Ƽ������ӻ��о�
+97,��,50,�����������ͻ�����¼������Ĳ���Ƚ�
+98,��,53,����ȫ����Դ�������ķֲ�ʽ��Դ�Ʒ���������ݷ���ƽ̨�о�
+99,��,97,��Դ�����������ݷ�����������
+100,��,69,�˹��������ڻ��������е�Ӧ�ý�չ
+101,ȫ,59,����ȫ����Դ�������ĵ��������ݻ�����ϵ�ܹ��ͱ�׼��ϵ�о�
+102,��,71,��Դ�������ؼ���������
+103,д,47,���ڹ�������ͼ���������д
+104,��,57,���ڷֲ�ʽ��������Դ�������Դ������ϵͳ
+105,��,101,CNN�Ի�������ͻ���¼��ı�������
+106,��,55,���ھ���������Ķ��ǩͼ���Զ���ע
+107,����,96,��������������
+108,˫,38,˫ͨ���ֿ�̬ͬ�˲���ɫͼ����ǿ�㷨
+109,��,31,�Ƽ�������Ӧ�ĺ���糡����ϵͳ������̼�ŷ�Ȩ�����Ż�����
+110,ͼ,58,����MapReduce��ͼ����෽��
+111,��,56,���ڿռ�ֲ�����ά�Զ����ν�ڷָ��㷨
+112,����,48,�������ݵ��ض�ͼ����˷���
+113,��,71,��Դ�����������Ŷ�ʶ�������������ģ��
+114,��,15,�����ݻ����µ���Դ��������չ���Ʒ���
+115,��,110,����Web����ţͼ��ʶ��ͼ����Ϣ����ϵͳ���о�
+116,ѧ,32,ѧ������д��ǳ��
+117,��,81,һ��Ƕ���ʽ΢��ϵͳ�Ķ��Է���
+118,չ,118,����ͼ���ͼ��ָ�������½�չ
+119,��,52,�����缰���ڹ��̹����е�Ӧ��
+120,��,118,���������·ֲ�ʽ�豸Э����������ϵͳƽ̨�������
+121,ƽ,24,ƽ�涨�������е����Ľ����б�
+122,��,97,��ɫͼ��ָ������
+123,΢,26,��������΢��ϵͳ�����Ľ�������
+124,��,26,�ӷ��վ����еĶ�β��ĵ����β��ġ������仹���ʱ䣿
+125,��,119,��Դ������������̬��ؼ�����
+126,̽,122,��������ý���滪��������
+127,��,22,����ͼ���������о���չ
+128,��,101,��С����·����ǩ�����㷨
+129,ǳ,74,ǳ��CNNЧӦ
+130,��,44,��Ⱦ���������Ķ�GPU���п��
+131,���,52,���ѧϰ�о�����
+132,��,69,���ڽ�������
+133,��,80,�������ʽ΢��ϵͳ�����Ľ����ж��뼫�޻���֧
+134,��,42,���������û�����ɿ���Ԥ�������е�Ӧ��
+135,��,69,��Դ�����������µĵ��������ݷ�չ����
+136,��,39,�˲���Ƹϵͳ������뿪��
+137,��,29,����������
+138,��,49,�����緢չ����
+139,��,104,һ�ֻ���CNN��Ƶ�˶�����ָ���о�
+140,��,86,����ϸ���������ͼ���Ե��ȡ�㷨�о�
+141,ϸ,86,����ϸ���������Ӧ���о�
+142,��,50,������������о���չ����
+143,��,81,���ڲ�����Ⱦ����������ͼ�����з���
+144,��,78,��������·�����������ͷֲ�ʽ��Դ���缼��
+145,·,71,��Դ����������Դ·����
+146,��,168,�����������о�����
+147,��,45,����ѧϰ�㷨������ѧϰ�е�Ӧ���о�
+148,��,35,������Դ�������Ĵ����ݹؼ������о�
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