riemanniangeometry2014.eu

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In machine learning, statistics and computer vision. In machine learning, statistics and computer vision. Saturday, December 13, 2014. In Montreal, Canada. In the existing literature, there are two common approaches for exploiting data geometry when the data is assumed to lie on a Riemannian manifold. Successful applications of these approaches are numerous and range from brain imaging and low rank matrix completion to computer vision tasks such as object detection and tracking. This workshop focuses on ...

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In machine learning, statistics and computer vision. In machine learning, statistics and computer vision. Saturday, December 13, 2014. In Montreal, Canada. In the existing literature, there are two common approaches for exploiting data geometry when the data is assumed to lie on a Riemannian manifold. Successful applications of these approaches are numerous and range from brain imaging and low rank matrix completion to computer vision tasks such as object detection and tracking. This workshop focuses on ...

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Overview | riemanniangeometry2014.eu Reviews

https://riemanniangeometry2014.eu

In machine learning, statistics and computer vision. In machine learning, statistics and computer vision. Saturday, December 13, 2014. In Montreal, Canada. In the existing literature, there are two common approaches for exploiting data geometry when the data is assumed to lie on a Riemannian manifold. Successful applications of these approaches are numerous and range from brain imaging and low rank matrix completion to computer vision tasks such as object detection and tracking. This workshop focuses on ...

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riemanniangeometry2014.eu

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Schedule

http://www.riemanniangeometry2014.eu/index.php/schedule

In machine learning, statistics and computer vision. Google Calendar Version is provided at the bottom of this page. 8:45 AM - 9:00 AM. 9:00 AM - 10:00 AM. The Geometrical Foundations of Hamiltonian Monte Carlo. 10:00 AM - 10:30 AM. 10:30 AM - 11:30 AM. Probabilistic Geodesic Models for Regression and Dimensionality Reduction on Riemannian Manifolds. 11:30 AM 12:30 PM. Kernels on Riemannian Manifolds. I will talk about recent results from a number of people in my group on Riemannian manifolds in computer...

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Invited Speakers

http://www.riemanniangeometry2014.eu/index.php/invited-speakers

In machine learning, statistics and computer vision. Michael Betancourt originally did his PhD in physics at MIT and then moved into statistics with a short stint at Columbia University with Andrew Gelman and then as a postdoctoral research associate with Mark Girolami, first at University College London and now at the University of Warwick. Dr Hartley worked at the General Electric Research and Development Center from 1985 to 2001. During the period 1985-1988, he was involved in the design and imple...

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Format

http://www.riemanniangeometry2014.eu/index.php/format

In machine learning, statistics and computer vision. The workshop will consist of invited talks only, followed by a panel discussion together with the speakers. Two half-hour periods, one in the morning and one in the afternoon, will be allocated for breaks and informal discussions. Contact: minh.haquang@iit.it.

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Organizers

http://www.riemanniangeometry2014.eu/index.php/organizers

In machine learning, statistics and computer vision. Istituto Italiano di Tecnologia, Genova, Italy). Minh Ha Quang is Researcher in Pattern Analysis and Computer Vision at the Istituto Italiano di Tecnologia (IIT) in Genova, Italy. He received his PhD in Mathematics from Brown University, USA, in May 2006. His research interests are in applied and computational functional analysis, machine learning, and statistics, with applications in computer vision and image processing.

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Vikas Sindhwani

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riemanniangeometry2014.eu

Overview

In machine learning, statistics and computer vision. In machine learning, statistics and computer vision. Saturday, December 13, 2014. In Montreal, Canada. In the existing literature, there are two common approaches for exploiting data geometry when the data is assumed to lie on a Riemannian manifold. Successful applications of these approaches are numerous and range from brain imaging and low rank matrix completion to computer vision tasks such as object detection and tracking. This workshop focuses on ...

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