{"id":68,"date":"2016-05-11T13:43:37","date_gmt":"2016-05-11T11:43:37","guid":{"rendered":"http:\/\/project.inria.fr\/deeplearning\/?page_id=68"},"modified":"2018-01-25T13:36:56","modified_gmt":"2018-01-25T12:36:56","slug":"sessions","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/deeplearning\/sessions\/","title":{"rendered":"Sessions"},"content":{"rendered":"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Date<\/strong><\/th>\nTopic<\/strong><\/th>\nRecommended readings<\/strong><\/th>\nSlides<\/strong><\/th>\n<\/tr>\n
2016-06-02, 10am, room A103<\/td>\nIntroduction to Neural Networks<\/td>\nBishop<\/a>, Chapter 5<\/td>\nslides<\/a> (Pablo Mesejo, St\u00e9phane Lathuili\u00e8re)<\/td>\n<\/tr>\n
2016-06-10, 10am, room F107<\/td>\nIntroduction to Neural Networks<\/td>\nBishop<\/a>, Chapter 5<\/td>\nslides<\/a> (St\u00e9phane Lathuili\u00e8re, Pablo Mesejo)<\/td>\n<\/tr>\n
2016-07-01, 10am, room F107<\/td>\nIntroduction to Convolutional Neural Networks<\/td>\nGoodfellow et al., Deep Learning<\/a>, Chapter 9<\/p>\n

Visualizing and Understanding Convolutional Networks<\/a><\/td>\n

slides1<\/a> slides2<\/a> (Vicky Kalogeiton, Shreyas Saxena)<\/td>\n<\/tr>\n
2016-09-22, 10am, room A103<\/td>\nUnsupervised Deep Learning<\/td>\nGenerative Adversarial Networks<\/a>, Auto-Encoding Variational Bayes<\/a>, Tutorial on Variational Autoencoders<\/a><\/td>\nslides1<\/a> slides2<\/a> slides3<\/a> (Daan Wynen, Jakob Verbeek)<\/td>\n<\/tr>\n
2016-09-30, 10am, room F108<\/td>\nIntroduction to Recurrent Neural Networks<\/td>\nGraves,\u00a0 Supervised Sequence Labelling with Recurrent Neural Networks<\/a>, Ch 3-3.2.4 and Ch 4<\/p>\n

Goodfellow et al., Deep Learning<\/a>, Chapter 10-10.3<\/td>\n

slides<\/a> (Marco Pedersoli, Thomas Lucas)<\/td>\n<\/tr>\n
2016-11-4, 10am, room F107<\/td>\nDeep Feedforward Networks<\/td>\nGoodfellow et al., Deep Learning<\/a>, Chapter 6<\/td>\nslides<\/a> (Benoit Mass\u00e9, Dionyssos Kounades-Bastian)<\/td>\n<\/tr>\n
2016-11-17, 15’30, room F107<\/td>\nRegularization for Deep Learning<\/td>\nGoodfellow et al., Deep Learning<\/a>, Chapter 7<\/td>\nslides<\/a> (Gildas Mazo)<\/td>\n<\/tr>\n
2016-12-13, 10am, room F108<\/td>\nOptimization for Training Deep Models<\/td>\nGoodfellow et al., Deep Learning<\/a>, Chapter 8<\/td>\nslides<\/a> (Henrique Morimitsu)<\/td>\n<\/tr>\n
2017-01-25, 10am, room F107<\/td>\nDeep Learning Libraries<\/td>\n–<\/td>\nslides<\/a> (Konstantin Shmelkov, Pauline Luc, Thomas Lucas, Vicky Kalogeiton, St\u00e9phane Lathuili\u00e8re)<\/td>\n<\/tr>\n
2017-10-03, 3pm, room C208<\/td>\nDeep Learning Papers<\/td>\nGenerative Adversarial Nets<\/a>, Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields<\/a>, Deeply-Learned Part-Aligned Representations for Person Re-Identification<\/a>, Pose-driven Deep Convolutional Model for Person Re-identification<\/a>, Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network<\/a>, Deep Forest: Towards An Alternative to Deep Neural Networks<\/a><\/td>\nSylvain Guy, Guillaume Delorme, Radu Horaud, Vincent Drouard, St\u00e9phane Lathuili\u00e8re, Benoit Mass\u00e9<\/td>\n<\/tr>\n
2017-10-17, 3pm, room C208<\/td>\nDeep Learning Papers<\/td>\nDetect to Track and Track to Detect<\/a>, Interpretable Convolutional Neural Networks<\/a>, Beyond triplet loss: a deep quadruplet network for person re-identification<\/a>, Unsupervised Image-to-Image Translation Network<\/a>, A Brief Survey of Deep Reinforcement Learning<\/a><\/td>\nSylvain Guy, Xavier Alameda, Benoit Mass\u00e9, St\u00e9phane Lathuili\u00e8re, Pablo Mesejo<\/td>\n<\/tr>\n
2017-11-07, 2pm, room F108<\/td>\nDeep Learning Papers<\/td>\nDeformable Convolutional Networks<\/a>, Encouraging LSTMs to Anticipate Actions Very Early<\/a>, Genetic CNN<\/a>, Rethinking Reprojection: Closing the Loop for Pose-Aware Shape Reconstruction From a Single Image<\/a>, Learning a Policy from Streaming Videos with Reinforcement Learning<\/a>, Learning From Noisy Labels With Distillation<\/a><\/td>\nRadu Horaud, Guillaume Delorme, Benoit Mass\u00e9, Vincent Drouard, Yutong Ban, St\u00e9phane Lathuili\u00e8re<\/td>\n<\/tr>\n
2017-11-21, 3pm, room F108<\/td>\nDeep Learning Papers<\/td>\nFollowing Gaze in Video<\/a>, Sliced Wasserstein Distance for Learning Gaussian Mixture Models<\/a>, Data Augmentation Generative Adversarial Networks<\/a>, Bayesian GAN<\/a>, SSH: Single Stage Headless Face Detector<\/a>, SafetyNet: Detecting and Rejecting Adversarial Examples Robustly<\/a><\/td>\nBenoit Mass\u00e9, Yutong Ban, St\u00e9phane Lathuili\u00e8re, Hongliang Lu, Sylvain Guy, Guillaume Delorme<\/td>\n<\/tr>\n
2017-12-05, 3pm, room F108<\/td>\nDeep Learning Papers<\/td>\nAre GANs Created Equal? A Large-Scale Study<\/a>, Deep clustering: Discriminative embeddings for segmentation and separation<\/a>, How far are we from solving the 2D & 3D Face Alignment problem?<\/a>, StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation<\/a>, Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning<\/a><\/td>\nSt\u00e9phane Lathuili\u00e8re, Laurent Girin, Benoit Mass\u00e9, Guillaume Delorme, Yutong Ban<\/td>\n<\/tr>\n
2018-01-23, 3pm, room C208<\/td>\nDeep Learning Papers<\/td>\nMask R-CNN<\/a>, Domain-Adversarial Training of Neural Networks<\/a>, Camera Style Adaptation for Person Re-identification<\/a>, Gradients explode – Deep Networks are shallow – ResNet explained <\/a><\/td>\nYutong Ban, Guillaume Delorme, Guillaume Delorme, \u00d3scar G\u00f3mez<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

LaTeX template<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Date Topic Recommended readings Slides 2016-06-02, 10am, room A103 Introduction to Neural Networks Bishop, Chapter 5 slides (Pablo Mesejo, St\u00e9phane Lathuili\u00e8re) 2016-06-10, 10am, room F107 Introduction to Neural Networks Bishop, Chapter 5 slides (St\u00e9phane Lathuili\u00e8re, Pablo Mesejo) 2016-07-01, 10am, room F107 Introduction to Convolutional Neural Networks Goodfellow et al., Deep\u2026<\/p>\n

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