Results 61 to 70 of about 5,652,238 (291)
Gaussian Processes for Regression [PDF]
, 1995 The Bayesian analysis of neural networks is difficult because a sim ple prior over weights implies a complex prior distribution over functions. In this paper we investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian anal ysis for fixed values of hyperparameters to be carried out exactly using matrix ...
Williams, C., Rasmussen, C.
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Regression with Gaussian Processes [PDF]
, 1997 The Bayesian analysis of neural networks is difficult because the prior over functions has a complex form, leading to implementations that either make approximations or use Monte Carlo integration techniques. In this paper I investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis to be carried out ...
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FEBS Letters, EarlyView.Encapsulins are protein nanocompartments that play an important role in iron storage. In the Myxococcus xanthus encapsulin system, two cargo proteins called EncB and EncC contribute to iron mineralization. Here, we show that EncB and EncC generate iron‐containing minerals with distinct chemical compositions, suggesting that the composition of stored ...
Harry B. McDowell +2 more
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Gaussian Processes and Fast Matrix-Vector Multiplies [PDF]
, 2009 Gaussian processes (GPs) provide a flexible framework for probabilistic regression. The necessary computations involve standard matrix operations.
Murray, Iain, Murray, Iain; id_orcid
core
Distributed Gaussian Processes With Uncertain Inputs
IEEE AccessGaussian Process regression is a powerful non-parametric approach that facilitates probabilistic uncertainty quantification in machine learning. Distributed Gaussian Process (DGP) methods offer scalable solutions by dividing data among multiple GP models
Peter L. Green
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WLAN monopole antenna design by Siamese convolutional neural network and KNN exploiting Gaussian process [PDF]
MATEC Web of ConferencesIn the process of antenna design, surrogate models can generally be used, but modeling requires a large number of samples. Although full wave electromagnetic simulation software can handle this task, obtaining a large number of samples is time-consuming,
Tian Yubo, Meng Fei
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Prospecting the protein design landscape
FEBS Letters, EarlyView.This review outlines the current state of various protein design approaches. We discuss the current possibilities enabled by recently released tools, highlight future avenues to pursue in protein design, and underscore the crucial role of key databases and resources for successful protein design workflows.
Jakob R. Riccabona +4 more
wiley +1 more source
Sensors, 2019 In this paper, we propose a novel approach to undertake the colorimetric camera characterization procedure based on a Gaussian process (GP). GPs are powerful and flexible nonparametric models for multivariate nonlinear functions. To validate the GP model,
Adolfo Molada-Tebar +3 more
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On the Construction of Some Fractional Stochastic Gompertz Models
Mathematics, 2020 The aim of this paper is the construction of stochastic versions for some fractional Gompertz curves. To do this, we first study a class of linear fractional-integral stochastic equations, proving existence and uniqueness of a Gaussian solution.
Giacomo Ascione, Enrica Pirozzi
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CoRR, 2013 Appears in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI2000)
Nir Friedman, Iftach Nachman
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