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database-subject.csv

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