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Deep Learning and Reinforcement Learning Summer School, Toronto 201830 Videos · Jul 24, 2018 Deep 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, graphics, he
Deep Learning (DLSS) and Reinforcement Learning (RLSS) Summer School, Montreal 201737 Videos · Jun 25, 2017 Deep 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 discover
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 Videos · Jul 31, 2016 Deep 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 datasets b
Deep Learning Summer School, Montreal 201530 Videos · Aug 2, 2015 Deep 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 datasets by
20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), New York 201439 Videos · Aug 24, 2014 KDD 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
In this paper we describe a novel extension of the support vector machine, called the deep support vector machine (DSVM). The original SVM has a single layer with kernel functions and is therefore a Read more
Machine learning algorithms are becoming increasingly important in our daily life. However, training on very large scale datasets is usually very slow. FPGA is a reconfigurable platform that can achie Read more
Information geometry emerged from studies on invariant properties of a manifold of probability distributions. It includes convex analysis and its duality as a special but important part. Here, we begi Read more
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
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Chang Huang,Bogdan Alexe,Carolina Galleguillos,Ramakrishna Kakarala,Zdenek Kalal,Tai-Peng Tian,Sid Ying-Ze Bao,Paul Schnitzspan,Myung Jin Choi,Javier Marin Tur,Shai Bagon,Li-Jia Li,Jose M. Alvarez Lopez
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
CREATED BY Jožef Stefan Institute Centre for Knowledge Transfer and Information Technologies
Machine Learning Summer School (MLSS), Cambridge 200920 Videos · Aug 27, 2009 The 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 in the
Generative Models for Visual Objects and Object Recognition via Bayesian Inference
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