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NIPS
2007
15 years 3 months ago
Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods
Learning in real-world domains often requires to deal with continuous state and action spaces. Although many solutions have been proposed to apply Reinforcement Learning algorithm...
Alessandro Lazaric, Marcello Restelli, Andrea Bona...
118
Voted
JMLR
2010
195views more  JMLR 2010»
15 years 5 days ago
Online Learning for Matrix Factorization and Sparse Coding
Sparse coding—that is, modelling data vectors as sparse linear combinations of basis elements—is widely used in machine learning, neuroscience, signal processing, and statisti...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
ICRA
2003
IEEE
222views Robotics» more  ICRA 2003»
15 years 7 months ago
Path planning using learned constraints and preferences
— In this paper we present a novel method for robot path planning based on learning motion patterns. A motion pattern is defined as the path that results from applying a set of ...
Gregory Dudek, Saul Simhon
CVPR
2012
IEEE
13 years 4 months ago
Submodular dictionary learning for sparse coding
A greedy-based approach to learn a compact and discriminative dictionary for sparse representation is presented. We propose an objective function consisting of two components: ent...
Zhuolin Jiang, Guangxiao Zhang, Larry S. Davis
SIGGRAPH
2003
ACM
15 years 7 months ago
Consistent illumination within optical see-through augmented environments
We present techniques which create a consistent illumination between real and virtual objects inside an application specific optical see-through display: the Virtual Showcase. We ...
Oliver Bimber, Anselm Grundhöfer, Gordon Wetz...