Deep Learning: Models and Optimization : 8 séances de cours + 4 séances de TD
– elementary blocks from signal processing and statistics: spatial and temporal convolutions, activation functions, compositions
– automatic differentiation: gradients, jacobians
TD1: implementation of backprop in 2-layer network.
– review of a few famous nets for vision applications: AlexNet, Resnet,…
– stochastic optimization of parameters for non-convex problems (RMSprop, ADAM etc..)
TD2: Survey of automatic differentiation frameworks, vanilla NN training on Cifar10
– theory: convex models for simple two-layer perceptrons; network structure optimization
– recurrent networks and the vanishing gradient problem, LSTM, memory and attention mechanisms.
TD3: LSTM and other recurrent networks on time series data.
– deep networks in action: GANs and VAEs
– applications to structured data: graph NN.
TD4: GAN and VAE.
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- 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.
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- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, DavidWarde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In NIPS, 2014.
- Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. Speech recognition with deep recurrent neural networks. In ICASSP, 2013.
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- Diederik P. Kingma and Jimmy Lei Ba. Adam: a method for stochastic optimization. In ICLR, 2015.
- Diederik P. Kingma and Max Welling. Auto-encoding variational bayes. In ICLR, 2014.
- Honglak Lee, Peter Pham, Yan Largman, and Andrew Y. Ng. Unsupervised feature learning for audio classification using convolutional deep belief networks. In NIPS, 2009.
- 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.
- Pauline Luc, Natalia Neverova, Camille Couprie, Jakob Verbeek, and Yann LeCun. Predicting deeper into the future of semantic segmentation. In ICCV, 2017.
- 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.
- Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. On the di_culty of training recurrent neural networks. Technical report, Université de Montréal, 2012.
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- Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. In ICLR, 2016.
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- Ruslan Salakhutdinov and Geoffrey E. Hinton. Semantic hashing. In SIGIR, 2017.
- Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015.
- Casper Kaae Sonderby, Tapani Raiko, Maale Lars, Soren Kaae Sonderby, and Ole Winther. Ladder variational autoencoders. In NIPS, 2016.
- Christian Szegedy, Vincent Vanhoucke, Sergey Ioe, Jonathon Shlens, and ZbigniewWojna. Rethinking the inception architecture for computer vision. In CVPR, 2016.