ABCDEFGHIJKLMNOPQRSTUVWXYZ
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RankTitleArxiv urlCitationPublish date
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1Deep Residual Learning for Image Recognition
http://arxiv.org/abs/1512.03385v1
161012015/12/10
3
2
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
http://arxiv.org/abs/1502.03167v3
74902015/2/11
4
3Caffe: Convolutional Architecture for Fast Feature Embedding
http://arxiv.org/abs/1408.5093v1
73522014/6/20
5
4Sequence to Sequence Learning with Neural Networks
http://arxiv.org/abs/1409.3215v3
50482014/9/10
6
5
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
http://arxiv.org/abs/1603.04467v2
43432016/3/14
7
6
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
http://arxiv.org/abs/1502.01852v1
32842015/2/6
8
7Representation Learning: A Review and New Perspectives
http://arxiv.org/abs/1206.5538v3
29152012/6/24
9
8Deep Learning in Neural Networks: An Overview
http://arxiv.org/abs/1404.7828v4
28542014/4/30
10
9
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
http://arxiv.org/abs/1511.06434v2
27282015/11/19
11
10TensorFlow: A system for large-scale machine learning
http://arxiv.org/abs/1605.08695v2
23552016/5/27
12
11FaceNet: A Unified Embedding for Face Recognition and Clustering
http://arxiv.org/abs/1503.03832v3
20542015/3/12
13
12
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
http://arxiv.org/abs/1312.6229v4
20372013/12/21
14
13
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
http://arxiv.org/abs/1310.1531v1
20282013/10/6
15
14Two-Stream Convolutional Networks for Action Recognition in Videos
http://arxiv.org/abs/1406.2199v2
18662014/6/9
16
15Intriguing properties of neural networks
http://arxiv.org/abs/1312.6199v4
17102013/12/21
17
16
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
http://arxiv.org/abs/1412.3555v1
16922014/12/11
18
17
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
http://arxiv.org/abs/1606.00915v2
16792016/6/2
19
18How transferable are features in deep neural networks?
http://arxiv.org/abs/1411.1792v1
16702014/11/6
20
19Playing Atari with Deep Reinforcement Learning
http://arxiv.org/abs/1312.5602v1
15812013/12/19
21
20Identity Mappings in Deep Residual Networks
http://arxiv.org/abs/1603.05027v3
14942016/3/16
22
21Multi-column Deep Neural Networks for Image Classification
http://arxiv.org/abs/1202.2745v1
14852012/2/13
23
22
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
http://arxiv.org/abs/1510.00149v5
14842015/10/1
24
23Learning Spatiotemporal Features with 3D Convolutional Networks
http://arxiv.org/abs/1412.0767v4
14122014/12/2
25
24
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
http://arxiv.org/abs/1511.00561v3
13432015/11/2
26
25Asynchronous Methods for Deep Reinforcement Learning
http://arxiv.org/abs/1602.01783v2
12832016/2/4
27
26
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
http://arxiv.org/abs/1412.7062v4
12812014/12/22
28
27DeepWalk: Online Learning of Social Representations
http://arxiv.org/abs/1403.6652v2
12492014/3/26
29
28Network In Network
http://arxiv.org/abs/1312.4400v3
12162013/12/16
30
29The Cityscapes Dataset for Semantic Urban Scene Understanding
http://arxiv.org/abs/1604.01685v2
11212016/4/6
31
30Deep Learning Face Attributes in the Wild
http://arxiv.org/abs/1411.7766v3
11122014/11/28
32
31WaveNet: A Generative Model for Raw Audio
http://arxiv.org/abs/1609.03499v2
10612016/9/12
33
32Image Super-Resolution Using Deep Convolutional Networks
http://arxiv.org/abs/1501.00092v3
10542014/12/31
34
33Conditional Random Fields as Recurrent Neural Networks
http://arxiv.org/abs/1502.03240v3
10282015/2/11
35
34Distilling the Knowledge in a Neural Network
http://arxiv.org/abs/1503.02531v1
9992015/3/9
36
35Theano: new features and speed improvements
http://arxiv.org/abs/1211.5590v1
9732012/11/23
37
36Feature Pyramid Networks for Object Detection
http://arxiv.org/abs/1612.03144v2
8722016/12/9
38
37
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
http://arxiv.org/abs/1511.07289v5
8442015/11/23
39
38Character-level Convolutional Networks for Text Classification
http://arxiv.org/abs/1509.01626v3
8402015/9/4
40
39Accurate Image Super-Resolution Using Very Deep Convolutional Networks
http://arxiv.org/abs/1511.04587v2
8252015/11/14
41
40End-to-End Training of Deep Visuomotor Policies
http://arxiv.org/abs/1504.00702v5
8092015/4/2
42
41Wide Residual Networks
http://arxiv.org/abs/1605.07146v4
8042016/5/23
43
42Learning Deep Features for Discriminative Localization
http://arxiv.org/abs/1512.04150v1
8002015/12/14
44
43Teaching Machines to Read and Comprehend
http://arxiv.org/abs/1506.03340v3
7892015/6/10
45
44Deep Learning Face Representation by Joint Identification-Verification
http://arxiv.org/abs/1406.4773v1
7722014/6/18
46
45DeepPose: Human Pose Estimation via Deep Neural Networks
http://arxiv.org/abs/1312.4659v3
7682013/12/17
47
46DRAW: A Recurrent Neural Network For Image Generation
http://arxiv.org/abs/1502.04623v2
7582015/2/16
48
47
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
http://arxiv.org/abs/1506.02142v6
7162015/6/6
49
48Semi-Supervised Learning with Deep Generative Models
http://arxiv.org/abs/1406.5298v2
7042014/6/20
50
49Understanding deep learning requires rethinking generalization
http://arxiv.org/abs/1611.03530v2
7002016/11/10
51
50Generative Adversarial Text to Image Synthesis
http://arxiv.org/abs/1605.05396v2
6972016/5/17
52
513D ShapeNets: A Deep Representation for Volumetric Shapes
http://arxiv.org/abs/1406.5670v3
6932014/6/22
53
52Xception: Deep Learning with Depthwise Separable Convolutions
http://arxiv.org/abs/1610.02357v3
6882016/10/7
54
53
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
http://arxiv.org/abs/1612.00593v2
6762016/12/2
55
54Deep Reinforcement Learning with Double Q-learning
http://arxiv.org/abs/1509.06461v3
6742015/9/22
56
55Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
http://arxiv.org/abs/1512.02595v1
6592015/12/8
57
56Domain-Adversarial Training of Neural Networks
http://arxiv.org/abs/1505.07818v4
6542015/5/28
58
57
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
http://arxiv.org/abs/1511.00363v3
6512015/11/2
59
58Learning Transferable Features with Deep Adaptation Networks
http://arxiv.org/abs/1502.02791v2
6072015/2/10
60
59Unsupervised Domain Adaptation by Backpropagation
http://arxiv.org/abs/1409.7495v2
6022014/9/26
61
60
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
http://arxiv.org/abs/1512.01274v1
6012015/12/3
62
61DeepFool: a simple and accurate method to fool deep neural networks
http://arxiv.org/abs/1511.04599v3
5912015/11/14
63
62Wasserstein GAN
http://arxiv.org/abs/1701.07875v3
5882017/1/26
64
63Pixel Recurrent Neural Networks
http://arxiv.org/abs/1601.06759v3
5882016/1/25
65
64Deep contextualized word representations
http://arxiv.org/abs/1802.05365v2
5872018/2/15
66
65
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
http://arxiv.org/abs/1606.09375v3
5792016/6/30
67
66
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
http://arxiv.org/abs/1602.03409v1
5672016/2/10
68
67
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
http://arxiv.org/abs/1511.04508v2
5642015/11/14
69
68
Practical recommendations for gradient-based training of deep architectures
http://arxiv.org/abs/1206.5533v2
5602012/6/24
70
69
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
http://arxiv.org/abs/1608.00859v1
5562016/8/2
71
70Deep multi-scale video prediction beyond mean square error
http://arxiv.org/abs/1511.05440v6
5542015/11/17
72
71Convolutional Sequence to Sequence Learning
http://arxiv.org/abs/1705.03122v3
5522017/5/8
73
72Deep Speech: Scaling up end-to-end speech recognition
http://arxiv.org/abs/1412.5567v2
5382014/12/17
74
73Towards Deep Learning Models Resistant to Adversarial Attacks
http://arxiv.org/abs/1706.06083v3
5342017/6/19
75
74Bag of Tricks for Efficient Text Classification
http://arxiv.org/abs/1607.01759v3
5322016/7/6
76
75
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
http://arxiv.org/abs/1608.03981v1
5302016/8/13
77
76The Limitations of Deep Learning in Adversarial Settings
http://arxiv.org/abs/1511.07528v1
5302015/11/24
78
77Matching Networks for One Shot Learning
http://arxiv.org/abs/1606.04080v2
5292016/6/13
79
78Learning Face Representation from Scratch
http://arxiv.org/abs/1411.7923v1
5272014/11/28
80
79A Survey on Deep Learning in Medical Image Analysis
http://arxiv.org/abs/1702.05747v2
5202017/2/19
81
80Understanding Neural Networks Through Deep Visualization
http://arxiv.org/abs/1506.06579v1
5202015/6/22
82
81
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
http://arxiv.org/abs/1609.05158v2
5162016/9/16
83
82Deep Learning with Limited Numerical Precision
http://arxiv.org/abs/1502.02551v1
5132015/2/9
84
83cuDNN: Efficient Primitives for Deep Learning
http://arxiv.org/abs/1410.0759v3
5092014/10/3
85
84Deeply-Supervised Nets
http://arxiv.org/abs/1409.5185v2
5092014/9/18
86
85
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
http://arxiv.org/abs/1604.02878v1
5032016/4/11
87
86From Captions to Visual Concepts and Back
http://arxiv.org/abs/1411.4952v3
5012014/11/18
88
87Do Deep Nets Really Need to be Deep?
http://arxiv.org/abs/1312.6184v7
4962013/12/21
89
88Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors
http://arxiv.org/abs/1505.04868v1
4942015/5/19
90
89
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
http://arxiv.org/abs/1512.05287v5
4872015/12/16
91
90FitNets: Hints for Thin Deep Nets
http://arxiv.org/abs/1412.6550v4
4832014/12/19
92
91Spectral Networks and Locally Connected Networks on Graphs
http://arxiv.org/abs/1312.6203v3
4772013/12/21
93
92Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
http://arxiv.org/abs/1703.03400v3
4672017/3/9
94
93
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
http://arxiv.org/abs/1406.2572v1
4592014/6/10
95
94
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
http://arxiv.org/abs/1312.6120v3
4552013/12/20
96
95Prioritized Experience Replay
http://arxiv.org/abs/1511.05952v4
4522015/11/18
97
96Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
http://arxiv.org/abs/1412.6632v5
4462014/12/20
98
97Training Very Deep Networks
http://arxiv.org/abs/1507.06228v2
4412015/7/22
99
98Dueling Network Architectures for Deep Reinforcement Learning
http://arxiv.org/abs/1511.06581v3
4412015/11/20
100
99
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
http://arxiv.org/abs/1411.2539v1
4312014/11/10