Results 181 to 190 of about 110,828 (307)

Fast Gaussian Process Regression Using KD-Trees [PDF]

The computation required for Gaussian process regression with n training examples is about O(n^3) during training and O(n) for each prediction. This makes Gaussian process regression too slow for large datasets.
Shen, Yirong, Ng, Andrew, Seeger, Matthias   +2 more
core  

Lower Limb Joint Torque Prediction Using Long Short-Term Memory Network and Gaussian Process Regression. [PDF]

Sensors (Basel), 2023
Wang M, Chen Z, Zhan H, Zhang J, Wu X, Jiang D, Guo Q.   +6 more
europepmc   +1 more source

Cross‐Modal Denoising and Integration of Spatial Multi‐Omics Data with CANDIES

Advanced Science, EarlyView.
In this paper, we introduce CANDIES, which leverages a conditional diffusion model and contrastive learning to effectively denoise and integrate spatial multi‐omics data. We conduct extensive evaluations on diverse synthetic and real datasets, CANDIES shows superior performance on various downstream tasks, including denoising, spatial domain ...
Ye Liu, Wanpeng Zou, Yuekai Li, Jiayi Wang, Mingxuan Cai, Hongmin Cai   +5 more
wiley   +1 more source

Learning curves for Gaussian process

, 1999
We consider the problem of calculating learning curves (i.e., average generalization performance) of Gaussian processes used for regression. On the basis of a simple expression for the generalization error, in terms of the eigenvalue decomposition of ...
Regression Approximations And, Anason Halees, Peter Sollich   +2 more
core  

Scalable Gaussian process regression via median posterior inference for estimating the health effects of an environmental mixture. [PDF]

Biometrics
Sonabend-W A, Zhang J, Castro E, Schwartz J, Coull BA, Lu J.   +5 more
europepmc   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

Gaussian Process Regression for Tail Vehicle Departure Time Prediction at Signalized Intersections Using UAV Trajectory Data. [PDF]

Sensors (Basel)
Lu K, Liu Z, Zhang R, Xia Q, Wang R.
europepmc   +1 more source

Correlated product of experts for sparse Gaussian process regression. [PDF]

Mach Learn, 2023
Schürch M, Azzimonti D, Benavoli A, Zaffalon M.   +3 more
europepmc   +1 more source

A Molecular Engineering Strategy to Fine‐Tune Phototoxicity of AIE Probes for Super‐Resolution Imaging of Mitochondrial Cristae Dynamics

Advanced Science, EarlyView.
Regulation of molecular packing, phototoxicity, and ROS output of AIE photosensitizers is achieved through carboxylation and alkyl‐chain‐length tuning. The probe designed via this strategy enables super‐resolution visualization of inner‐mitochondrial‐membrane cristae dynamics under controlled oxidative stress, and fluorescence‐lifetime imaging of ...
Kongqi Chen, Mengjie Wang, Chuen Kam, Zhiming Wang, Qinrong Zhang, Sijie Chen   +5 more
wiley   +1 more source

Gaussian Process Regression for Value-Censored Functional and Longitudinal Data. [PDF]

Stat Med
Gorm Hoffmann A, Thorn Ekstrøm C, Zeymer Christoffersen B, Kryger Jensen A.   +3 more
europepmc   +1 more source