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177
Voted
ALT
2005
Springer
2005
Springer
PAC-Learnability of Probabilistic Deterministic Finite State Automata in Terms of Variation Distance
16 years 3 months ago
We consider the problem of PAC-learning distributions over strings, represented by probabilistic deterministic finite automata (PDFAs). PDFAs are a probabilistic model for the gen...
174
Voted
ICASSP
2009
IEEE
16 years 1 months ago
2009
IEEE
In this paper, we propose a Bayesian model and a Monte Carlo Markov chain (MCMC) algorithm for reconstructing images that consist of only few non-zero pixels. An appropriate distr...
167
click to vote
ICASSP
2009
IEEE
16 years 1 months ago
2009
IEEE
Phase Noise (PN) is a serious challenge in wireless transmission systems as it can cause significant degradation of the system performance. Recent publications propose iterative ...
177
Voted
QEST
2009
IEEE
16 years 1 months ago
2009
IEEE
This paper provides an experimental study of the efficiency of simulation-based model-checking algorithms for continuous-time Markov chains by comparing: MRMC – the only tool t...
219
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IEEECGIV
2009
IEEE
16 years 1 months ago
2009
IEEE
The theory of compressive sampling involves making random linear projections of a signal. Provided signal is sparse in some basis, small number of such measurements preserves the ...