ENSAE Paris - École d'ingénieurs pour l'économie, la data science, la finance et l'actuariat
ENSAE Paris - École d'ingénieurs pour l'économie, la data science, la finance et l'actuariat
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Deep Learning: Models and Optimization

Teacher

Olivier KOCH

KOCH Olivier

ECTS:
3

Course Hours:
12

Tutorials Hours:
6

Language:
French

Examination Modality:
écrit+CC

References

  1. Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. Segnet: a deep convolutional encoderdecoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 12:2481-2495, December 2017.
  2. Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In EMNLP, 2014.
  3. Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 2:295-307, February 2016.
  4. John Duchi, Elad Hazan, and Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, vol. 12:2121-2159, July 2011.
  5. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, DavidWarde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In NIPS, 2014.
  6. Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. Speech recognition with deep recurrent neural networks. In ICASSP, 2013.
  7. Geoffrey Hinton. Training products of experts by minimizing contrastive divergence. Neural Computation, vol. 14, no. 8:1771-1800, August 2002.
  8. Geoffrey Hinton. A practical guide to training restricted boltzmann machines. Technical report, University of Toronto, August 2010.
  9. Sepp Hochreiter and Jurgen Schmidhuber. Long-short term memory. Neural Computation, vol. 9, no. 8:1735-1780, 1997.
  10. Diederik P. Kingma and Jimmy Lei Ba. Adam: a method for stochastic optimization. In ICLR, 2015.
  11. Diederik P. Kingma and Max Welling. Auto-encoding variational bayes. In ICLR, 2014.
  12. Honglak Lee, Peter Pham, Yan Largman, and Andrew Y. Ng. Unsupervised feature learning for audio classification using convolutional deep belief networks. In NIPS, 2009.
  13. Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. Enhanced deep residual networks for single image super-resolution. In CVPR Workshops, 2017.
  14. Pauline Luc, Natalia Neverova, Camille Couprie, Jakob Verbeek, and Yann LeCun. Predicting deeper into the future of semantic segmentation. In ICCV, 2017.
  15. Abdel-rahman Mohamed, George E. Dahl, and Geo_rey Hinton. Acoustic modeling using deep belief networks. IEEE Transactions on Audio, Speech, and Language Processing, vol. 20, issue 1:14-22, January 2012.
  16. Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. On the di_culty of training recurrent neural networks. Technical report, Université de Montréal, 2012.
  17. Ning Qian. On the momentum in gradient descent learning algorithms. Neural Networks, vol. 12, issue 1:145-151, January 1999.
  18. Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. In ICLR, 2016.
  19. David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. Learning representations by backpropagating errors. Nature, vol. 323:533-536, October 1986.
  20. Ruslan Salakhutdinov and Geoffrey E. Hinton. Semantic hashing. In SIGIR, 2017.
  21. Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015.
  22. Casper Kaae Sonderby, Tapani Raiko, Maale Lars, Soren Kaae Sonderby, and Ole Winther. Ladder variational autoencoders. In NIPS, 2016.
  23. Christian Szegedy, Vincent Vanhoucke, Sergey Ioe, Jonathon Shlens, and ZbigniewWojna. Rethinking the inception architecture for computer vision. In CVPR, 2016.
     
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