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Št. zadetkov: 6
Gaussian Processes for Monocular 3D People tracking
Raquel Urtasun
Video in druga učna gradiva
Oznake: computer science;machine learning;gaussian processes
We advocate the use of Gaussian Processes (GPs) to learn prior models of human pose and motion for 3D people tracking. The Gaussian Process Latent variable model (GPLVM) provides a low-dimensional embedding of the human pose, and defines a density function that gives higher probability to poses clos ...
Leto: 2006 Vir: videolectures.net
Topologically-Constrained Latent Variable Models
Raquel Urtasun
Video in druga učna gradiva
Oznake: computer science;machine learning
In dimensionality reduction approaches, the data are typically embedded in a Euclidean latent space. However for some data sets this is inappropriate. For example, in human motion data we expect latent spaces that are cylindrical or a toroidal, that are poorly captured with a Euclidean space. In thi ...
Leto: 2008 Vir: videolectures.net
Non-Linear Matrix Factorization with Gaussian Processes
Raquel Urtasun
Video in druga učna gradiva
Oznake: computer science;machine learning
A popular approach to collaborative filtering is matrix factorization. In this paper we consider the "probabilistic matrix factorization" and by taking a latent variable model perspective we show its equivalence to Bayesian PCA. This inspires us to consider probabilistic PCA and its non-linear exten ...
Leto: 2009 Vir: videolectures.net
A Gaussian Process View on MKL
Raquel Urtasun
Video in druga učna gradiva
Oznake: computer science;machine learning;gaussian processes
Gaussian processes (GPs) provide an appealing probabilistic framework for multiple kernel learning (MKL). For more than a decade, it has been common practice to learn the well known sum-of-kernels by, for example, maximum likelihood estimation. In this talk, I’ll first introduce the sum-of-kernels G ...
Leto: 2010 Vir: videolectures.net
Incorporating Structure in Deep Learning
Raquel Urtasun
Video in druga učna gradiva
Oznake: computer science;machine learning
Deep learning algorithms attempt to model high-level abstractions of the data using architectures composed of multiple non-linear transformations. A multiplicity of variants have been proposed and shown to be extremely successful in a wide variety of applications including computer vision, speech re ...
Leto: 2016 Vir: videolectures.net
Panel discussion
Guillaume Obozinski , Raquel Urtasun , Massimiliano Pontil , Kilian Weinberger
Video in druga učna gradiva
Oznake: computer science;machine learning;kernel methods;multiple kernel learning
Leto: 2010 Vir: videolectures.net
Št. zadetkov: 6
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