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Deep Learning and Reinforcement Learning Summer School, Toronto 201829 Lectures · Jul 24, 2018 AboutDeep neural networks are a powerful method for automatically learning distributed representations at multiple levels of abstraction. Over the past decade, they have dramatically pushed forward the state-of-the-art in domains as diverse as vision, language understanding, robotics, game playing, graph
Deep Learning (DLSS) and Reinforcement Learning (RLSS) Summer School, Montreal 201736 Lectures · Jun 25, 2017 AboutDeep neural networks that learn to represent data in multiple layers of increasing abstraction have dramatically improved the state-of-the-art for speech recognition, object recognition, object detection, predicting the activity of drug molecules, and many other tasks. Deep learning d
In this tutorial I will discuss how reinforcement learning (RL) can be combined with deep learning (DL). There are several ways to combine DL and RL together, including value-based, policy-based, and Read more
Deep Learning Summer School, Montreal 201635 Lectures · Jul 31, 2016 AboutDeep neural networks that learn to represent data in multiple layers of increasing abstraction have dramatically improved the state-of-the-art for speech recognition, object recognition, object detection, predicting the activity of drug molecules, and many other tasks. Deep learning discovers intricate structure in large dat
Deep Learning Summer School, Montreal 201530 Lectures · Aug 2, 2015 AboutDeep neural networks that learn to represent data in multiple layers of increasing abstraction have dramatically improved the state-of-the-art for speech recognition, object recognition, object detection, predicting the activity of drug molecules, and many other tasks. Deep learning discovers intricate structure in large data
20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), New York 201438 Lectures · Aug 24, 2014 AboutKDD 2014, a premier interdisciplinary conference, brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data. Read More Related categories
International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines (ROKS): theory and applications, Leuven 2013 author: Marco Wiering, Faculty of Mathematics and Natural Sciences, University of Groningen published: Aug. 26, 2013, recorded: July 2013, views: 21314
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Report a problem or upload files If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data. Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status. Information geometry emerged from studies on invariant properties of a manifold of
Anomaly detection corresponds to discovery of events that typically do not conform to expected normal behavior. Such events are often referred to as anomalies, outliers, exceptions, deviations, aberra Read more
Best Application Paper Award Winner Behavioral targeting (BT) leverages historical user behavior to select the ads most relevant to users to display. The state-of-the-art of BT derives a linear Poiss Read more
At 10 years of age, there is little doubt that the Semantic Web is an engineering success, with substantial (and growing) take-up in business, government and media. However, as a scientific field, hav Read more
Basics for Statistical Machine Learning Linear Algebra Basics00:00
In this article, we propose fast subtree kernels on graphs. On graphs with n nodes and m edges and maximum degree d, these kernels comparing subtrees of height h can be computed in O(mh), whereas th Read more
23rd IEEE Conference on Computer Vision and Pattern Recognition 2010 - San Francisco98 Lectures · Jun 13, 2010
Bayesian approaches to learning problems have many virtues, including their ability to make use of prior knowledge and their ability to link related sources of information, but they also have many vic Read more
Complex probabilistic models of unlabeled data can be created by combining simpler models. Mixture models are obtained by averaging the densities of simpler models and "products of experts" are obtain Read more
A basic premise behind the study of large networks is that interaction leads to complex collective behavior. In our work we found very interesting and counterintuitive patterns for time evolving net Read more
en-deen-esen-fren-slenen-zhOff0.250.50.7511.251.51.752Introduction To Bayesian InferencePublished on Nov 02, 2009369919 ViewsChristopher BishopMLSS 2009 - Cambridge Bookmark You need to login Share Embed Related categoriesBayesian LearningWatch other parts1. PartIntroduction To Bayesian Inference Christopher BishopNov 02, 2009 369919 Views 2. PartIntroduction To Bayesian Inference Christopher Bish
en-deen-esen-fren-slenen-zhOff0.250.50.7511.251.51.752Bayesian or Frequentist, Which Are You?Published on Nov 02, 2009108319 ViewsMichael I. JordanMLSS 2009 - Cambridge Bookmark You need to login Share Embed Related categoriesBayesian LearningWatch other parts1. PartBayesian or Frequentist, Which Are You? Michael I. JordanNov 02, 2009 108319 Views 2. PartBayesian or Frequentist, Which Are You? Mic
en-deen-esen-fren-slenen-zhOff0.250.50.7511.251.51.752Topic ModelsPublished on Nov 02, 2009312721 ViewsDavid BleiMLSS 2009 - Cambridge Bookmark You need to login Share Embed Related categoriesMachine LearningWatch other parts1. PartTopic Models David BleiNov 02, 2009 312721 Views 2. PartTopic Models David BleiNov 02, 2009 312721 Views
en-deen-esen-fren-slenen-zhOff0.250.50.7511.251.51.752Markov Chain Monte CarloPublished on Nov 02, 2009237009 ViewsIain MurrayMLSS 2009 - Cambridge Bookmark You need to login Share Embed Related categoriesMonte Carlo MethodsStatisticsWatch other parts1. PartMarkov Chain Monte Carlo Iain MurrayNov 02, 2009 237009 Views 2. PartMarkov Chain Monte Carlo Iain MurrayNov 02, 2009 237009 Views
Machine Learning Summer School (MLSS), Cambridge 200920 Lectures · Aug 27, 2009 AboutThe 13th Machine Learning Summer School was held in Cambridge, UK. This year's edition was organized by the University of Cambridge, Microsoft Research and PASCAL. The school offered an overview of basic and advanced topics in machine learning through theoretical and practical lectures given by leading researchers
en-deen-esen-fren-pten-slenen-zhOff0.250.50.7511.251.51.752Statistical Learning as the Ultimate Agile Development ToolPublished on Nov 19, 200820938 ViewsPeter NorvigSessions Bookmark You need to login Share Embed Related categoriesKnowledge Management
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en-deen-esen-fren-slenen-zhOff0.250.50.7511.251.51.752Semisupervised Learning ApproachesPublished on Feb 25, 200785053 ViewsTom MitchellMachine Learning over Text and Images Bookmark You need to login Share Embed Related categoriesMachine LearningSemi-supervised Learning
Generative Models for Visual Objects and Object Recognition via Bayesian Inference
Building and operating large-scale information retrieval systems used by hundreds of millions of people around the world provides a number of interesting challenges. Designing such systems requires ma Read more
Second ACM International Conference on Web Search and Data Mining - WSDM 200934 Lectures · Feb 9, 2009 AboutWSDM (pronounced "wisdom") is a young ACM conference intended to be the publication venue for research in the areas of search and data mining. Indeed, the pace of innovation in these areas prevents proper coverage by conferences of broader scope. The high attendance at the first WSDM, held a
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