Results 41 to 50 of about 3,267,859 (298)
Sparse model construction using coordinate descent optimization [PDF]
, 2013 We propose a new sparse model construction method aimed at maximizing a model’s generalisation capability for a large class of linear-in-the-parameters models.
Xia Hong +8 more
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A novel sparse representation algorithm for AIS real-time signals
EURASIP Journal on Wireless Communications and Networking, 2018 Sparse representation of signals based on a redundant dictionary is a new signal representation theory. Recent research activities in this field have concentrated mainly on the study of dictionary design and sparse decomposition algorithms.
Shuaiheng Huai, Shufang Zhang
doaj +1 more source
Actually Sparse Variational Gaussian Processes
CoRR, 2023 Gaussian processes (GPs) are typically criticised for their unfavourable scaling in both computational and memory requirements. For large datasets, sparse GPs reduce these demands by conditioning on a small set of inducing variables designed to summarise the data.
Harry Jake Cunningham +4 more
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The cosparse analysis model and algorithms [PDF]
, 2013 After a decade of extensive study of the sparse representation synthesis model, we can safely say that this is a mature and stable field, with clear theoretical foundations, and appealing applications.
Gribonval, Rémi +8 more
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Adaptive Sparse Gaussian Process
IEEE Transactions on Neural Networks and Learning SystemsAdaptive learning is necessary for non-stationary environments where the learning machine needs to forget past data distribution. Efficient algorithms require a compact model update to not grow in computational burden with the incoming data and with the lowest possible computational cost for online parameter updating.
Vanessa Gómez-Verdejo +2 more
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Sparse Additive Gaussian Process Regression
J. Mach. Learn. Res., 2019 In this paper we introduce a novel model for Gaussian process (GP) regression in the fully Bayesian setting. Motivated by the ideas of sparsification, localization and Bayesian additive modeling, our model is built around a recursive partitioning (RP) scheme. Within each RP partition, a sparse GP (SGP) regression model is fitted.
Hengrui Luo +2 more
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Iterative thresholding for sparse approximations [PDF]
, 2008 Sparse signal expansions represent or approximate a signal using a small number of elements from a large collection of elementary waveforms. Finding the optimal sparse expansion is known to be NP hard in general and non-optimal strategies such as ...
Blumensath, T. +3 more
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Leida xuebao, 2018 This paper examines the processing of millimeter-wave imaging data based on sparse sampling and sparse array design for the rapid imaging of human security data.
Tian He, Li Daojing, Qi Chunchao
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Sparse Gaussian Neural Processes
CoRRProceedings of the 7th Symposium on Advances in Approximate Bayesian Inference, PMLR, 2025.
Tommy Rochussen, Vincent Fortuin
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