| Paper |
| Num |
Title |
Review |
Paper |
| 1 |
resNet[1] Deep Residual Learning for Image Recognition(2015) |
review |
paper |
| 2 |
resNet[2] Identity Mappings in Deep Residual Networks(2016) |
review |
paper |
| 3 |
resNet[3] Residual Networks Behave Like Ensembles of Relatively Shallow Networks(2016) |
review |
paper |
| 4 |
VDCNN[1] Very Deep Convolutional Networks for Text Classification(2016) |
review |
paper |
| 5 |
CoVe[1] Learned in Translation: Contextualized Word Vectors(2017) |
review |
paper |
| 6 |
wordCNN[1] Convolutional Neural Networks for Sentence Classification(2014) |
review |
paper |
| 7 |
wordCNN[2] Sensitivity Analysis of CNN sentence classification(2015) - Review(2015) |
review |
paper |
| 8 |
charCNN[1] Character level Convolutional Networks for Text Classification(2015) |
review |
paper |
| 9 |
LSTM[1] Long Short Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling(2014) |
review |
paper |
| 10 |
BLSTM[1] Bi-directional LSTM Recurrent Neural Network for Chinese Word Segmentation(2016) |
review |
paper |
| 11 |
DBLSTM[1] Hybrid Speech Recognition With Deep Bidirectional LSTM(2013) |
review |
paper |
| 12 |
Attention[1] Neural Machine Translation by Jointly Learning to Align and Translate(2014) |
review |
paper |
| 13 |
Attention[2] Effective Approaches to Attention based Neural Machine Translation(2015) |
review |
paper |
| 14 |
BiDAF[1] Editing Bidirectional Attention Flow for Machine Comprehension(2016) |
review |
paper |
| 15 |
Seq2Seq[1] Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation(2014) |
review |
paper |
| 16 |
Seq2Seq[2] Sequence to Sequence Learning with Neural Networks(2014) |
review |
paper |
| 17 |
GRU[1] Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling(2014) |
review |
paper |
| 18 |
charCNN[2] Character Aware Neural Language Model(2016) |
review |
paper |
| 19 |
char-word LSTM[1] Character Word LSTM Language Models(2017) |
review |
paper |
| 20 |
BLSTM-CNN-CRF[1] Character Word LSTM Language Models(2016) |
review |
paper |
| 21 |
Seq2Seq[3] A Persona Based Neural Conversation Model(2016) |
review |
paper |
| 22 |
QRNN[1] Quasi Recurrent Neural Networks(2016) |
review |
paper |
| 23 |
SRU[1] Training RNNs as Fast as CNNs(2017) |
review |
paper |
| 24 |
ByteNet[1] Neural Machine Translation in Linear Time(2017) |
review |
paper |
| 25 |
Inception[1] Going Deeper with Convolutions(2014) |
review |
paper |
| 26 |
Inception[2] Rethinking the Inception Architecture for Computer Vision(2015) |
review |
paper |
| 27 |
Inception[3] Inception v4, Inception ResNet and the Impact of Residual Connections on Learning(2016) |
review |
paper |
| 28 |
Xception[1] Xception: Deep Learning with Depthwise Separable Convolutions(2017) |
review |
paper |
| 29 |
sliceNet[1] Depthwise Separable Convolutions for Neural Machine Translation(2017) |
review |
paper |
| 30 |
denseNet[1] Densely Connected Convolutional Networks(2016) |
review |
paper |
| 31 |
Spark[1] Spark: Cluster Computing with Working Sets(2010) |
review |
paper |
| 32 |
Spark[2] Resilient Distributed Datasets: A Fault Tolerant Abstraction for In Memory Cluster Computing(2012) |
review |
paper |
| 33 |
Distributed DL[1] Parallel and Distributed Deep Learning(2016) |
review |
paper |
| 34 |
Distributed DL[2] Large Scale Distributed Deep Networks(2012) |
review |
paper |
| 35 |
AlexNet[1] ImageNet Classification with Deep Convolutional Neural Networks(2012) |
review |
paper |
| 36 |
Audio Style Transfer[1] Neural Style Transfer for Audio Spectrograms(2018) |
review |
paper |
| 37 |
Audio Style Transfer[2] Style Transfer for Prosodic Speech(2017) |
review |
paper |
| 38 |
Audio Style Transfer[3] Music Style Transfer: A Position Paper(2018) |
review |
paper |
| 39 |
Audio Style Transfer[4] AUDIO STYLE TRANSFER(2017) |
review |
paper |
| 40 |
WaveNet[1] WaveNet: A Generative Model for Raw Audio(2016) |
review |
paper |
| 41 |
Audio Style Transfer[5] A Universal Music Translation Network(2018) |
review |
paper |
| 42 |
Transformer[1] Attention Is All You Need(2017) |
review |
paper |
| 43 |
R-CNN[1] Rich feature hierarchies for accurate object detection and semantic segmentation(2014) |
review |
paper |
| 44 |
Fast R-CNN[1] Fast R CNN(2015) |
review |
paper |
| 45 |
Faster R-CNN[1] Faster R CNN: Towards Real Time Object Detection with Region Proposal Networks(2015) |
review |
paper |
| 46 |
YOLO[1] You Only Look Once: Unified, Real Time Object Detection(2015) |
review |
paper |
| 47 |
YOLO[2] YOLO9000: Better, Faster, Stronger(2016) |
review |
paper |
| 48 |
YOLO[3] YOLOv3: An Incremental Improvement(2018) |
review |
paper |
| 49 |
RetinaNet[1] Focal Loss for Dense Object Detection(2017) |
review |
paper |
| 50 |
SSD[1] SSD: Single Shot MultiBox Detector(2015) |
review |
paper |
| 51 |
DSSD[1] DSSD: Deconvolutional Single Shot Detector(2017) |
review |
paper |
| 52 |
Light-Head R-CNN[1] Light-Head R-CNN: In Defense of Two Stage Object Detector(2017) |
review |
paper |
| 53 |
R-FCN[1] R-FCN: Object Detection via Region based Fully Convolutional Networks(2016) |
review |
paper |
| 54 |
R-FCN[2] R-FCN++: Towards Accurate Region Based Fully Convolutional Networks for Object Detection(2018) |
review |
paper |
| 55 |
FPN[1] Feature Pyramid Networks for Object Detection(2016) |
review |
paper |