Results 11 to 20 of about 3,267,859 (298)
Bayesian modelling of music : algorithmic advances and experimental studies of shift-invariant sparse coding [PDF]
, 2005 PhDIn order to perform many signal processing tasks such as classification, pattern recognition and coding, it is helpful to specify a signal model in terms of meaningful signal structures.
Blumensath, Thomas
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Sparse-PE: A Performance-Efficient Processing Engine Core for Sparse Convolutional Neural Networks
IEEE Access, 2021 Sparse convolutional neural network (CNN) models reduce the massive compute and memory bandwidth requirements inherently present in dense CNNs without a significant loss in accuracy. Sparse CNNs, however, present their own set of challenges including non-
Mahmood Azhar Qureshi, Arslan Munir
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MMSE Estimation of Sparse Lévy Processes [PDF]
IEEE Transactions on Signal Processing, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ulugbek Kamilov +3 more
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Frame coherence and sparse signal processing [PDF]
2011 IEEE International Symposium on Information Theory Proceedings, 2011 The sparse signal processing literature often uses random sensing matrices to obtain performance guarantees. Unfortunately, in the real world, sensing matrices do not always come from random processes. It is therefore desirable to evaluate whether an arbitrary matrix, or frame, is suitable for sensing sparse signals.
Dustin G. Mixon +2 more
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Sparse representations in audio & music: from coding to source separation [PDF]
, 2010 —Sparse representations have proved a powerful toolin the analysis and processing of audio signals and already lieat the heart of popular coding standards such as MP3 andDolby AAC.
Davies, ME +15 more
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Sparse multiscale gaussian process regression [PDF]
Proceedings of the 25th international conference on Machine learning - ICML '08, 2008 Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of the g.p. with one of its two inputs fixed. We generalise this for the case of Gaussian covariance function, by basing our computations on m Gaussian basis functions with ...
Christian Walder +2 more
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New Directions In Sparse Sampling and Estimation For Underdetermined Systems [PDF]
, 2013 A central objective in signal processing is to infer meaningful information from a set of measurements or data. While most signal models have an overdetermined structure (the number of unknowns less than the number of equations), traditionally very few ...
Piya Pal, Pal, Piya
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Current Developments of Sparse Microwave Imaging
Leida xuebao, 2014 The sparse microwave imaging combines the sparse signal processing theory with radar imaging to obtain new theory, new system, and new methodology of microwave imaging.
Wu Yi-rong +5 more
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Dictionary learning with large step gradient descent for sparse representations [PDF]
, 2012 This is the accepted version of an article published in Lecture Notes in Computer Science Volume 7191, 2012, pp 231-238.
Boris Mailhé +6 more
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Explicit Object Representation by Sparse Neural Codes [PDF]
, 2008 Neurons have been identified in the human medial temporal lobe (MTL) that display a strong selectivity for only a few stimuli (such as familiar individuals or landmark buildings) out of perhaps 100 presented to the test subject.
Waydo, Stephen J.
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