吴恩达深度学习推荐论文列表[附论文链接]

从看到第四课开始，发现出现论文的频率高了不少，想着把这些论文都粗略的读下，就先把他们列在这里，如有不全烦请告知

第一课：NeuralNetworks & Deep Learning

并没有

第二课：Improving Deep Neural Networks :Hyperparameter tuning, Regularization and Optimization

Dropout

Srivastava, Nitish, et al. “Dropout: a simple way to prevent neural networks from overfitting.” The journal of machine learning research 15.1 (2014): 1929-1958.
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Adam

Kingma, Diederik P., and Jimmy Ba. “Adam: A method for stochastic optimization.” arXiv preprint arXiv:1412.6980 (2014).
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第三课：Structuring Machine Learning Projects

也没有

第四课：Convolutional Neural Networks

LeNet-5

LeCun, Yann, et al. “Gradient-based learning applied to document recognition.” Proceedings of the IEEE 86.11 (1998): 2278-2324.
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AlexNet

Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. “Imagenet classification with deep convolutional neural networks.” Advances in neural information processing systems. 2012.
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VGG-16

Simonyan, Karen, and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition.” arXiv preprint arXiv:1409.1556 (2014).
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Andrew Ng 推荐阅读顺序：
AlexNet > VGG > LeNet

ResNet

He, Kaiming, et al. “Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
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Network in network

Lin, Min, Qiang Chen, and Shuicheng Yan. “Network in network.” arXiv preprint arXiv:1312.4400 (2013).
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Inception Network

Szegedy, Christian, et al. “Going deeper with convolutions.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
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Object Detection

OverFeat

Sermanet, Pierre, et al. “Overfeat: Integrated recognition, localization and detection using convolutional networks.” arXiv preprint arXiv:1312.6229 (2013).
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YOLO

Redmon, Joseph, et al. “You only look once: Unified, real-time object detection.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
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R-CNN

Girshick, Ross, et al. “Rich feature hierarchies for accurate object detection and semantic segmentation.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.
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Fast R-CNN

Girshick, Ross. “Fast r-cnn.” Proceedings of the IEEE international conference on computer vision. 2015.
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Faster R-CNN

Ren, Shaoqing, et al. “Faster r-cnn: Towards real-time object detection with region proposal networks.” Advances in neural information processing systems. 2015.
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face recognition

Siamese Network

Taigman, Yaniv, et al. “Deepface: Closing the gap to human-level performance in face verification.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.
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"encoding"

FaceNet

Schroff, Florian, Dmitry Kalenichenko, and James Philbin. “Facenet: A unified embedding for face recognition and clustering.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
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Neural Style Transfer

Visualizing and understanding convolutional networks

Zeiler, Matthew D., and Rob Fergus. “Visualizing and understanding convolutional networks.” European conference on computer vision. Springer, Cham, 2014.
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可视化每个节点的激活情况

Neural Style Transfer

Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. “A neural algorithm of artistic style.” arXiv preprint arXiv:1508.06576 (2015).
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第五课：Sequence Models

第一周：循环序列模型

GRU

Cho, Kyunghyun, et al. “On the properties of neural machine translation: Encoder-decoder approaches.” arXiv preprint arXiv:1409.1259 (2014).
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Chung, Junyoung, et al. “Empirical evaluation of gated recurrent neural networks on sequence modeling.” arXiv preprint arXiv:1412.3555 (2014).
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LSTM

Hochreiter, Sepp, and Jürgen Schmidhuber. “Long short-term memory.” Neural computation 9.8 (1997): 1735-1780.
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第二周 自然语言处理

t-SNE

Maaten, Laurens van der, and Geoffrey Hinton. “Visualizing data using t-SNE.” Journal of machine learning research 9.Nov (2008): 2579-2605.
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讲的是如何可视化embedding数据的方法

Analogy reasoning

Mikolov, Tomas, Wen-tau Yih, and Geoffrey Zweig. “Linguistic regularities in continuous space word representations.” Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2013.
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embedding用于类比推理

Learing word embeddings

A neural probabilistic language model

Bengio, Yoshua, et al. “A neural probabilistic language model.” Journal of machine learning research 3.Feb (2003): 1137-1155.
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通过滑窗预测下一个词来优化embedding matrix

Word2Vec

Mikolov, Tomas, et al. “Efficient estimation of word representations in vector space.” arXiv preprint arXiv:1301.3781 (2013).
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Negative Sampling

Mikolov, Tomas, et al. “Distributed representations of words and phrases and their compositionality.” Advances in neural information processing systems. 2013.
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解决skipgram 中 softmax 计算成本高的问题

GloVe

Pennington, Jeffrey, Richard Socher, and Christopher Manning. “Glove: Global vectors for word representation.” Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014.
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Debiasing word embeddings

Bolukbasi, Tolga, et al. “Man is to computer programmer as woman is to homemaker? debiasing word embeddings.” Advances in neural information processing systems. 2016.
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消除embedding带来的词性的偏差

第三周 序列模型和注意力机制

Seq 2 Seq & machine translation

Sutskever, I., O. Vinyals, and Q. V. Le. “Sequence to sequence learning with neural networks.” Advances in NIPS (2014).
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Cho, Kyunghyun, et al. “Learning phrase representations using RNN encoder-decoder for statistical machine translation.” arXiv preprint arXiv:1406.1078 (2014).
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Image captioning

Mao, Junhua, et al. “Deep captioning with multimodal recurrent neural networks (m-rnn).” arXiv preprint arXiv:1412.6632 (2014).
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Vinyals, Oriol, et al. “Show and tell: A neural image caption generator.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
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Karpathy, Andrej, and Li Fei-Fei. “Deep visual-semantic alignments for generating image descriptions.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
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Bleu

Papineni, Kishore, et al. “BLEU: a method for automatic evaluation of machine translation.” Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, 2002.
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Attention

Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. “Neural machine translation by jointly learning to align and translate.” arXiv preprint arXiv:1409.0473 (2014).
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Xu, Kelvin, et al. “Show, attend and tell: Neural image caption generation with visual attention.” International conference on machine learning. 2015.
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CTC cost

Graves, Alex, et al. “Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks.” Proceedings of the 23rd international conference on Machine learning. ACM, 2006.
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--------------------------终于看完啦~~，接下来会读其中一部分文章，有精力的话会写个简单的摘要，懒癌，就酱~~~-------------------