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FLAIRS
2004
13 years 11 months ago
State Space Reduction For Hierarchical Reinforcement Learning
er provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, ...
Mehran Asadi, Manfred Huber
ICML
2010
IEEE
13 years 10 months ago
FAB-MAP: Appearance-Based Place Recognition and Mapping using a Learned Visual Vocabulary Model
We present an overview of FAB-MAP, an algorithm for place recognition and mapping developed for infrastructure-free mobile robot navigation in large environments. The system allow...
Mark Joseph Cummins, Paul M. Newman
ICRA
2010
IEEE
143views Robotics» more  ICRA 2010»
13 years 8 months ago
Apprenticeship learning via soft local homomorphisms
Abstract— We consider the problem of apprenticeship learning when the expert’s demonstration covers only a small part of a large state space. Inverse Reinforcement Learning (IR...
Abdeslam Boularias, Brahim Chaib-draa
SIGIR
2011
ACM
13 years 25 days ago
Pseudo test collections for learning web search ranking functions
Test collections are the primary drivers of progress in information retrieval. They provide a yardstick for assessing the effectiveness of ranking functions in an automatic, rapi...
Nima Asadi, Donald Metzler, Tamer Elsayed, Jimmy L...
ICCV
2011
IEEE
12 years 10 months ago
Adaptive Deconvolutional Networks for Mid and High Level Feature Learning
We present a hierarchical model that learns image decompositions via alternating layers of convolutional sparse coding and max pooling. When trained on natural images, the layers ...
Matthew D. Zeiler, Graham W. Taylor, Rob Fergus