Update(2023): I am not maintaining this repo anymore.

Deep-Learning-Papers-I-have-read

Note : I take the responsibility that all the information present here is true and I have read all the given mentioned papers to the best of my knowledge.

A. General Deep Learning and Classical Machine Learning

1. Approximation by Superpositions of a Sigmoidal Function* G. Cybenko https://pdfs.semanticscholar.org/05ce/b32839c26c8d2cb38d5529cf7720a68c3fab.pdf

2. GradientBased Learning Applied to Document Recognition Yann LeCun Leon Bottou Yoshua Bengio and Patrick Haner http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf

3. Interpretable Convolutional Neural Networks via Feedforward Design,C.-C. Jay Kuo, Min Zhang, Siyang Li, Jiali Duan, Yueru Chen. https://arxiv.org/abs/1810.02786

4. Understanding Convolutional Neural Networks with A Mathematical Model,C.-C. Jay Kuo https://arxiv.org/abs/1609.04112

5. Deep Learning and the Information Bottleneck Principle Naftali Tishby, Noga Zaslavsky https://arxiv.org/abs/1503.02406

6. Adversarial Training Methods for Semi-Supervised Text Classification Takeru Miyato, Andrew M. Dai, Ian Goodfellow https://arxiv.org/pdf/1605.07725.pdf

7. Attention Is All You Need Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin https://arxiv.org/abs/1706.03762

8. Visualizing Data using t-SNE Laurens van der Maaten, Geoffrey Hinton http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf

9. Neighbourhood Components Analysis Jacob Goldberger, Sam Roweis, Geoff Hinton, Ruslan Salakhutdinov https://www.cs.toronto.edu/~hinton/absps/nca.pdf

10. How transferable are features in deep neural networks? Jason Yosinski,1 Jeff Clune,2 Yoshua Bengio,3 and Hod Lipson4 https://arxiv.org/pdf/1411.1792.pdf

B. Generative Models

1. LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS, Andrew Brock, Jeff Donahue, Karen Simonyan https://arxiv.org/pdf/1809.11096.pdf

2. ClusterGAN : Latent Space Clustering in Generative Adversarial Networks, Sudipto Mukherjee, Himanshu Asnani, Eugene Lin, Sreeram Kannan https://arxiv.org/abs/1809.03627

3. Generative Adversarial Nets, Ian J. Goodfellow∗, Jean Pouget-Abadie†, Mehdi Mirza, Bing Xu, David Warde-Farley,Sherjil Ozair‡, Aaron Courville, Yoshua Bengio https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf

4. NIPS 2016 Tutorial: Generative Adversarial Networks, Ian Goodfellow https://arxiv.org/abs/1701.00160

5. Wasserstein GAN Martin Arjovsky, Soumith Chintala, Léon Bottou https://arxiv.org/abs/1701.07875

6. Least Squares Generative Adversarial Networks Xudong Mao, Qing Li, Haoran Xie, Raymond Y.K. Lau, Zhen Wang, Stephen Paul Smolley https://arxiv.org/abs/1611.04076

7. Spectral Normalization for Generative Adversarial Networks Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida https://arxiv.org/abs/1802.05957

8. Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Shin Ishii https://arxiv.org/abs/1704.03976

9. cGANs with Projection Discriminator Takeru Miyato, Masanori Koyama https://arxiv.org/abs/1802.05637

10. Auto-Encoding Variational Bayes Diederik P Kingma, Max Welling https://arxiv.org/abs/1312.6114

11. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, Alexander Lerchner https://openreview.net/forum?id=Sy2fzU9gl

12. Self-Attention Generative Adversarial Networks Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena https://arxiv.org/abs/1805.08318

13. Disentangling by Factorising Hyunjik Kim, Andriy Mnih https://arxiv.org/pdf/1802.05983.pdf

14. Isolating Sources of Disentanglement in Variational Autoencoders Ricky T. Q. Chen, Xuechen Li, Roger Grosse, David Duvenaud https://arxiv.org/abs/1802.04942

15. WaveNet: A Generative Model for Raw Audio Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, Koray Kavukcuoglu https://arxiv.org/abs/1609.03499

16. Mode matching in GANs through latent space learning and inversion Deepak Mishra, Prathosh A. P., Aravind Jayendran, Varun Srivastava, Santanu Chaudhury https://arxiv.org/abs/1811.03692

17. Autoencoders, Unsupervised Learning, and Deep Architectures http://proceedings.mlr.press/v27/baldi12a/baldi12a.pdf

18. GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec https://arxiv.org/abs/1802.08773

19. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel https://arxiv.org/abs/1606.03657

20. Semi-supervised Learning with Deep Generative Models, Diederik P. Kingma∗ , Danilo J. Rezende† , Shakir Mohamed† , Max Welling https://papers.nips.cc/paper/5352-semi-supervised-learning-with-deep-generative-models.pdf

21. ADVERSARIALLY LEARNED INFERENCE Vincent Dumoulin1 , Ishmael Belghazi1 , Ben Poole2 Olivier Mastropietro1 , Alex Lamb1 , Martin Arjovsky3 Aaron Courville1† https://arxiv.org/pdf/1606.00704.pdf

22. High-Fidelity Image Generation With Fewer Labels Mario Lucic * Michael Tschannen * Marvin Ritter * Xiaohua Zhai Olivier Bachem Sylvain Gelly http://proceedings.mlr.press/v97/lucic19a/lucic19a.pdf

23. Self-Supervised GANs via Auxiliary Rotation Loss Ting Chen∗, Xiaohua Zhai, Marvin Ritter https://arxiv.org/abs/1811.11212

24. Disconnected Manifold Learning for Generative Adversarial Networks Mahyar Khayatkhoei, Ahmed Elgammal, Maneesh Singh https://arxiv.org/abs/1806.00880

25. Improved Techniques for Training GANs Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen https://arxiv.org/abs/1606.03498

26. EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES Ian J. Goodfellow, Jonathon Shlens & Christian Szegedy https://arxiv.org/pdf/1412.6572.pdf

C. Language Models, Text and Document Classification using Deep Learning

1. Sequence to Sequence Learning with Neural Networks Ilya Sutskever, Oriol Vinyals, Quoc V. Le https://arxiv.org/abs/1409.3215

2. Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification Peng Zhou, Wei Shi, Jun Tian, Zhenyu Qi∗ , Bingchen Li, Hongwei Hao, Bo Xu https://www.aclweb.org/anthology/P16-2034

3. Hierarchical Attention Networks for Document Classification Zichao Yang1 , Diyi Yang1 , Chris Dyer1 , Xiaodong He2 , Alex Smola1 , Eduard Hovy1 https://aclweb.org/anthology/N16-1174

4. Convolutional Neural Networks for Sentence Classification Yoon Kim https://arxiv.org/abs/1408.5882

5. ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs Wenpeng Yin, Hinrich Schütze, Bing Xiang, Bowen Zhou https://arxiv.org/abs/1512.05193

6. RMDL: Random Multimodel Deep Learning for Classification Kamran Kowsari, Mojtaba Heidarysafa, Donald E. Brown, Kiana Jafari Meimandi, Laura E. Barnes https://arxiv.org/abs/1805.01890

7. Neural Machine Translation by Jointly Learning to Align and Translate Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio https://arxiv.org/abs/1409.0473

8. Variational Deep Semantic Hashing for Text Documents Suthee Chaidaroon, Yi Fang https://arxiv.org/pdf/1708.03436.pdf

9. Doc2hash: Learning Discrete Latent Variables for Document Retrieval Yifei Zhang, Hao Zhu https://www.aclweb.org/anthology/N19-1232