Results 31 to 40 of about 139,689 (265)
Input Dependent Sparse Gaussian Processes
CoRR, 2021 Gaussian Processes (GPs) are Bayesian models that provide uncertainty estimates associated to the predictions made. They are also very flexible due to their non-parametric nature. Nevertheless, GPs suffer from poor scalability as the number of training instances N increases. More precisely, they have a cubic cost with respect to $N$.
Bahram Jafrasteh +2 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
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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Sparse Modeling for Image and Vision Processing [PDF]
Foundations and Trends® in Computer Graphics and Vision, 2014 In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection—that is, automatically selecting a simple model among a large collection of them.
Julien Mairal +2 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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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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Sparse within Sparse Gaussian Processes using Neighbor Information
CoRR, 2020 10 ...
Gia-Lac Tran +3 more
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A Review of Radar Signal Processing Based on Sparse Recovery
Leida xuebaoWith the growing demand for radar target detection, Sparse Recovery (SR) technology based on the Compressive Sensing (CS) model has been widely used in radar signal processing.
Yinghui QUAN +6 more
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Natural Killer Cells in Paediatric Soft Tissue Sarcomas: A Systematic Review
Pediatric Blood &Cancer, EarlyView.ABSTRACT Paediatric soft tissue sarcomas (pSTS) are a rare and heterogeneous group of malignant tumours arising in tissues of mesenchymal origin. The role of natural killer (NK) cells in pSTS remains poorly understood, with evidence fragmented across small preclinical studies and early‐phase clinical trials.
Raya Dean +7 more
wiley +1 more source