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» Recovering signals from lowpass data
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ECCV
2010
Springer
14 years 9 months ago
Hybrid Compressive Sampling via a New Total Variation TVL1
Compressive sampling (CS) is aimed at acquiring a signal or image from data which is deemed insufficient by Nyquist/Shannon sampling theorem. Its main idea is to recover a signal ...
Xianbiao Shu, Narendra Ahuja
94
Voted
NIPS
1997
15 years 13 days ago
Learning Generative Models with the Up-Propagation Algorithm
Up-propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden var...
Jong-Hoon Oh, H. Sebastian Seung
110
Voted
BMCBI
2006
203views more  BMCBI 2006»
14 years 11 months ago
Independent component analysis reveals new and biologically significant structures in micro array data
Background: An alternative to standard approaches to uncover biologically meaningful structures in micro array data is to treat the data as a blind source separation (BSS) problem...
Attila Frigyesi, Srinivas Veerla, David Lindgren, ...
NIPS
2008
15 years 16 days ago
Adaptive Template Matching with Shift-Invariant Semi-NMF
How does one extract unknown but stereotypical events that are linearly superimposed within a signal with variable latencies and variable amplitudes? One could think of using temp...
Jonathan Le Roux, Alain de Cheveigné, Lucas...
ICA
2010
Springer
14 years 9 months ago
Second Order Subspace Analysis and Simple Decompositions
Abstract. The recovery of the mixture of an N-dimensional signal generated by N independent processes is a well studied problem (see e.g. [1,10]) and robust algorithms that solve t...
Harold W. Gutch, Takanori Maehara, Fabian J. Theis