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Scalable greedy algorithms for transfer learning

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Geometric Variational Models for Inverse Problems in Imaging

Virginia Estellers Casas

This dissertation develops geometric variational models for different inverse problems in imaging that are ill-posed, designing at the same time efficient numerical algorithms to compute their solutions. Variational methods solve inverse problems by the fo ...

EPFL2013

Leveraging over prior knowledge for online learning of visual categories

Barbara Caputo, Tatiana Tommasi, Mohsen Kaboli

Open ended learning is a dynamic process based on the continuous analysis of new data, guided by past experience. On one side it is helpful to take advantage of prior knowledge when only few information on a new task is available (transfer learning). On th ...

2012

Joint Pose Estimator and Feature Learning for Object Detection

Pascal Fua, François Fleuret, Karim Ali, David Hasler

A new learning strategy for object detection is presented. The proposed scheme forgoes the need to train a collection of detectors dedicated to homogeneous families of poses, and instead learns a single classifier that has the inherent ability to deform ba ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

YaQ: An Architecture for Real-Time Navigation and Rendering of Varied Crowds

Daniel Thalmann, Jonathan Maim, Barbara Yersin

The YaQ software platform is a complete system dedicated to real-time crowd simulation and rendering. Fitting multiple application domains, such as video games and VR, YaQ aims to provide efficient algorithms to generate crowds comprising up to thousands o ...

Institute of Electrical and Electronics Engineers2009

World-set Decompositions: Expressiveness and Efficient Algorithms

Christoph Koch

Uncertain information is commonplace in real-world data management scenarios. The ability to represent large sets of possible instances (worlds) while supporting efficient storage and processing is an important challenge in this context. The recent formali ...

2007

MOTIF: AN EFFICIENT ALGORITHM FOR LEARNING TRANSLATION INVARIANT DICTIONARIES

Pierre Vandergheynst, Rémi Gribonval, Philippe Jost

The performances of approximation using redundant expansions rely on having dictionaries adapted to the signals. In natural high-dimensional data, the statistical dependencies are, most of the time, not obvious. Learning fundamental patterns is an alternat ...

2006

Prior Knowledge in Kernel Methods

Alexei Pozdnoukhov

Machine Learning is a modern and actively developing field of computer science, devoted to extracting and estimating dependencies from empirical data. It combines such fields as statistics, optimization theory and artificial intelligence. In practical task ...

École Polytechnique Fédérale de Lausanne2006

Prior knowledge in Kernel methods

Alexei Pozdnoukhov

EPFL2006

Tracking Atoms with Particles

Pierre Vandergheynst, Gianluca Monaci, Emilio Maggio

We present a general framework and an efficient algorithm for tracking relevant video structures. The structures to be tracked are implicitly defined by a Matching Pursuit procedure that extracts and ranks the most important image contours. Based on the ra ...

2006

Global mixed-integer dynamic optimization

Benoît Chachuat

Recent advances in process synthesis, design, operations, and control have created an increasing demand for efficient numerical algorithms for optimizing a dynamic system coupled with discrete decisions; these problems are termed mixed-integer dynamic opti ...

2005