Results 11 to 20 of about 139,689 (265)
Sparse On-Line Gaussian Processes [PDF]
Neural Computation, 2002 We develop an approach for sparse representations of gaussian process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations for large data sets. The method is based on a combination of a Bayesian on-line algorithm, together with a sequential construction of a relevant subsample of the data that fully ...
Csato, Lehel, Opper, Manfred
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A sparse expansion for deep Gaussian processes
IISE Transactions, 2023 In this work, we use Deep Gaussian Processes (DGPs) as statistical surrogates for stochastic processes with complex distributions. Conventional inferential methods for DGP models can suffer from high computational complexity as they require large-scale operations with kernel matrices for training and inference.
Liang Ding, Rui Tuo, Shahin Shahrampour
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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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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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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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Flash-Based Computing-in-Memory Architecture to Implement High-Precision Sparse Coding
Micromachines, 2023 To address the concerns with power consumption and processing efficiency in big-size data processing, sparse coding in computing-in-memory (CIM) architectures is gaining much more attention.
Yueran Qi +9 more
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Gaussian and sparse processes are limits of generalized Poisson processes [PDF]
Applied and Computational Harmonic Analysis, 2020 The theory of sparse stochastic processes offers a broad class of statistical models to study signals. In this framework, signals are represented as realizations of random processes that are solution of linear stochastic differential equations driven by white Lévy noises.
Fageot, Julien +2 more
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RSNN: A Software/Hardware Co-Optimized Framework for Sparse Convolutional Neural Networks on FPGAs
IEEE Access, 2021 Convolutional Neural Networks (CNNs) have been shown to be very useful in image recognition and other Artificial Intelligence (AI) applications, however, at the expense of intensive computation requirement.
Weijie You, Chang Wu
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Asynchronous processing of sparse signals
IET Signal Processing, 2014 Unlike synchronous processing, asynchronous processing is more efficient in biomedical and sensing networks applications as it is free from aliasing constraints and quantization error in the amplitude, it allows continuous–time processing and more importantly data is only acquired in significant parts of the signal. We consider signal decomposers based
Azime Can-Cimino +2 more
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Regularized Sparse Gaussian Processes
CoRR, 2019 Gaussian processes are a flexible Bayesian nonparametric modelling approach that has been widely applied but poses computational challenges. To address the poor scaling of exact inference methods, approximation methods based on sparse Gaussian processes (SGP) are attractive.
Rui Meng +3 more
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