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ESANN
2007
15 years 8 months ago
The Recurrent Control Neural Network
This paper presents our Recurrent Control Neural Network (RCNN), which is a model-based approach for a data-efficient modelling and control of reinforcement learning problems in di...
Anton Maximilian Schäfer, Steffen Udluft, Han...
192
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NIPS
2008
15 years 8 months ago
Sparse Signal Recovery Using Markov Random Fields
Compressive Sensing (CS) combines sampling and compression into a single subNyquist linear measurement process for sparse and compressible signals. In this paper, we extend the th...
Volkan Cevher, Marco F. Duarte, Chinmay Hegde, Ric...
UAI
2008
15 years 8 months ago
Projected Subgradient Methods for Learning Sparse Gaussians
Gaussian Markov random fields (GMRFs) are useful in a broad range of applications. In this paper we tackle the problem of learning a sparse GMRF in a high-dimensional space. Our a...
John Duchi, Stephen Gould, Daphne Koller
229
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ACL
2006
15 years 8 months ago
Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling
We present a new semi-supervised training procedure for conditional random fields (CRFs) that can be used to train sequence segmentors and labelers from a combination of labeled a...
Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Gre...
159
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DAGSTUHL
2006
15 years 8 months ago
Context Dependent Analysis of BioAmbients
BioAmbients is a derivative of mobile ambients that has shown promise of describing interesting features of the behaviour of biological systems. The technical contribution of this...
Henrik Pilegaard, Hanne Riis Nielson, Flemming Nie...