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| 1 | +id,label,images,subject |
| 2 | +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 |
| 56 | +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 |
| 64 | +63,PM,51,Probabilistic Non-Local Means |
| 65 | +64,PN,35,Production Strategy of Carbon Sensitive Products under Low-Carbon Policies |
| 66 | +65,PO,22,Research of Power Generation Right Transaction Scheduling Model Considering Carbon Emission Constraint Blocking |
| 67 | +66,PR,108,Palmprint recognition with Local Micro-structure Tetra Pattern |
| 68 | +67,PV,39,PVANet��Deep but Lightweight Neural Networks for Real-time Object Detection |
| 69 | +68,Q,95,Quantized Convolutional Neural Networks for Mobile Devices |
| 70 | +69,QN,121,Quantized neural networks��Training neural networks with low precision weights and activations |
| 71 | +70,R,82,Natural image statistics and neural representation |
| 72 | +71,RA,71,Refining Architectures of Deep Convolutional Neural Networks |
| 73 | +72,RD,41,Reshaping deep neural network for fast decoding by node-pruning |
| 74 | +73,RF,156,Rich feature hierarchies for accurate object detection and semantic segmentation Tech report |
| 75 | +74,RO,43,Restructuring of Deep Neural Network Acoustic Models with Singular Value Decomposition |
| 76 | +75,RS,123,Deep Residual Learning for Image Recognition |
| 77 | +76,S,39,Skew Correction and Line Extraction in Binarized Printed Text Images |
| 78 | +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 |
| 81 | +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 |
| 83 | +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 |
| 85 | +84,T,24,Ternary weight networks |
| 86 | +85,TR,63,Text Recognition for Information Retrieval in Images of Printed Circuit Boards |
| 87 | +86,TS,67,Outrageously Large Neural Networks��The Sparsely-Gated Mixture-of-Experts Layer |
| 88 | +87,TT,66,Trained Ternary Quantization |
| 89 | +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 |
| 92 | +91,X,36,XNOR-Net��ImageNet Classification Using Binary Convolutional Neural Networks |
| 93 | +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����˷��� |
| 114 | +113,��,71,��Դ�����������Ŷ�ʶ�������������ģ�� |
| 115 | +114,��,15,�����ݻ����µ���Դ��������չ���Ʒ��� |
| 116 | +115,��,110,����Web����ţͼ��ʶ��ͼ����Ϣ����ϵͳ���о� |
| 117 | +116,ѧ,32,ѧ������д��dz�� |
| 118 | +117,��,81,һ��Ƕ���ʽ��ϵͳ�Ķ��Է��� |
| 119 | +118,չ,118,����ͼ���ͼ��ָ�������½�չ |
| 120 | +119,��,52,�����缰���ڹ��̹����е�Ӧ�� |
| 121 | +120,��,118,���������·ֲ�ʽ�豸Э����������ϵͳƽ̨������� |
| 122 | +121,ƽ,24,ƽ�涨�������е����Ľ����б� |
| 123 | +122,��,97,��ɫͼ��ָ������ |
| 124 | +123,,26,����������ϵͳ�����Ľ������� |
| 125 | +124,��,26,�ӷ��վ����еĶ�β��ĵ����β��ġ������仹���ʱ䣿 |
| 126 | +125,��,119,��Դ������������̬��ؼ����� |
| 127 | +126,̽,122,��������ý���滪�������� |
| 128 | +127,��,22,����ͼ���������о���չ |
| 129 | +128,��,101,��С����·����ǩ�����㷨 |
| 130 | +129,dz,74,dz��CNNЧӦ |
| 131 | +130,��,44,��Ⱦ���������Ķ�GPU���п�� |
| 132 | +131,���,52,���ѧϰ�о����� |
| 133 | +132,��,69,���ڽ������� |
| 134 | +133,��,80,�������ʽ��ϵͳ�����Ľ����ж��뼫����֧ |
| 135 | +134,��,42,���������û�����ɿ���Ԥ�������е�Ӧ�� |
| 136 | +135,��,69,��Դ�����������µĵ��������ݷ�չ���� |
| 137 | +136,��,39,�˲���Ƹϵͳ������뿪�� |
| 138 | +137,��,29,���������� |
| 139 | +138,��,49,�����緢չ���� |
| 140 | +139,��,104,һ�ֻ���CNN��Ƶ�˶�����ָ���о� |
| 141 | +140,��,86,����ϸ���������ͼ���Ե��ȡ�㷨�о� |
| 142 | +141,ϸ,86,����ϸ���������Ӧ���о� |
| 143 | +142,��,50,������������о���չ���� |
| 144 | +143,��,81,���ڲ�����Ⱦ����������ͼ�����з��� |
| 145 | +144,��,78,��������·�����������ͷֲ�ʽ��Դ���缼�� |
| 146 | +145,·,71,��Դ����������Դ·���� |
| 147 | +146,��,168,��������������� |
| 148 | +147,��,45,����ѧϰ�㷨������ѧϰ�е�Ӧ���о� |
| 149 | +148,��,35,������Դ�������Ĵ����ݹؼ������о� |
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