PAPERS.md

DeepLearning.ai - Deep Learning Specialization

This is a list of research papers referenced by the Deep Learning Specialization course.

Convolutional Neural Networks

Week 2

Classic Networks

• Lacuna et al, 1998 - Gradient-based learning applied to document recognition
• Krizhevsky et al 2012 - ImageNet classification with deep convolutional neural networks
• Simony & Zisserman 2015 - Very deep convolutional networks for large scale image recognition - He et al, 2015

Resnets

• Lin et al, 2013 - Deep residual networks for image recognition

Networks in Networks and 1x1 Convolutions

• Szegedy et al, 2014 - Network in network - Going deeper with convolutions

Networks in Networks and 1x1 Convolutions

Min Lin, Qiang Chen, Shuicheng Yan - "Network In Network"

Inception Networks

• Christian Szegedy, and lots of others - "Going Deeper with Convolutions":

Week 3

Convolutional Implementation of Sliding Windows

• Servant et al, 2014 - OverFeat: Integrated recognition, localization and detection using convolutional networks

Bounding Box Predictions

• Redmon et al, 2014 - You Only Look Once: Unified real-time object detection
• Redmon et al, 2016 - YOLO9000: Better, Faster, Stronger

Region Proposals

• Girishik et al, 2013 - Rich feature hierarchies for accurate object detection and semantic segmentation
• Girshik, 2015 - Fast R-CNN
• Ren et al, 2016 - Faster R-CNN: Toward real-time object detection with region proposal networks

Week 4

Siamese Network

• Taigman et al, 2014 - DeepFace: Closing the Gap to Human-Level Performance in Face Verification

Triplet Loss

• Schroff et al. - FaceNet: A Unified Embedding for Face Recognition and Clustering

What are deep ConvNets learning?

• Zeiler and Fergus, 2013 - Visualizing and understanding convolutional networks

Neural Style

• Gates et al, 2015 - A neural algorithm of artistic style
• Harish Narayanan - Convolutional neural networks for artistic style transfer
• Log0, TensorFlow Implementation of "A Neural Algorithm of Artistic Style"

Image Recognition

• Karen Simonyan and Andrew Zisserman (2015). Very deep convolutional networks for large-scale image recognition

NLP Sequence Models

Week 1

GRU

• On the Properties of Neural Machine Translation: Encoder-Decoder Approaches - Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, Yoshua Bengio
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling - Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, Yoshua Bengio

LSTM

• LSTM - Schmidhuber and Hochreiter

Week 2

Skip-Grams, Hierarchical Softmax

• Efficient Estimation of Word Representations in Vector Space

Word Embeddings

• Linguistic Regularities in Continuous Space Word Representations - Tomas Mikolov, Wen-tau Yih, Geoffrey Zwei
• A Neural Probabilistic Language Model - Bengio et al

Negative Sampling

• Distributed Representations of Words and Phrases and their Compositionality Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean

Glove

• GloVe: Global Vectors for Word Representation Jeffrey Pennington, Richard Socher, Christopher D. Manning

Debaising Word Embeddings

• Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

Week 3

Sequence to Sequence Model

• Sequence to Sequence Learning with Neural Networks - Ilya Sutskever, Oriol Vinyals, Quoc V. Le

• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation - Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio

Image Captioning

• Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN) - Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Zhiheng Huang, Alan Yuille

• Show and Tell: A Neural Image Caption Generator - Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan

• Deep Visual-Semantic Alignments for Generating Image Descriptions - Andrej Karpathy, Li Fei-Fei

Bleu Score

• BLEU: a Method for Automatic Evaluation of Machine Translation Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu

Attention based intuation

• Neural Machine Translation by Jointly Learning to Align and Translate - Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio

• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention - Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, Yoshua Bengio

Speech Recognition

• Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks

Further Reading List

• http://deeplearning.net/reading-list/