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NIPS
1994
13 years 11 months ago
Generalization in Reinforcement Learning: Safely Approximating the Value Function
To appear in: G. Tesauro, D. S. Touretzky and T. K. Leen, eds., Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995. A straightforward approach to t...
Justin A. Boyan, Andrew W. Moore
ICASSP
2010
IEEE
13 years 10 months ago
Symmetrical EEG/FMRI fusion with spatially adaptive priors using variational distribution approximation
In this paper, we propose a symmetrical EEG/fMRI fusion algorithm which combines EEG and fMRI by means of a common generative model. The use of a total variation (TV) prior as wel...
Martin Luessi, S. Derin Babacan, Rafael Molina, Ja...
EMNLP
2010
13 years 8 months ago
Turbo Parsers: Dependency Parsing by Approximate Variational Inference
We present a unified view of two state-of-theart non-projective dependency parsers, both approximate: the loopy belief propagation parser of Smith and Eisner (2008) and the relaxe...
André F. T. Martins, Noah A. Smith, Eric P....
ICASSP
2011
IEEE
13 years 1 months ago
Compressive sampling with a successive approximation ADC architecture
This paper proposes a compressive sampling scheme based on random temporal sampling using a successive approximation register (SAR) ADC architecture. Variable wordlength data samp...
Chenchi Luo, James H. McClellan
ICASSP
2011
IEEE
13 years 1 months ago
Approximation of pattern transformation manifolds with parametric dictionaries
The construction of low-dimensional models explaining highdimensional signal observations provides concise and efficient data representations. In this paper, we focus on pattern ...
Elif Vural, Pascal Frossard