ENSAE Paris - École d'ingénieurs pour l'économie, la data science, la finance et l'actuariat

Deep Learning: Models and Optimization


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  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.
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  18. Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. In ICLR, 2016.
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  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.