Questions tagged [gaussian-process]

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Gaussian process regression for large dimensional input space

I am working for Gaussian process (GP) regression $y=f(\mathbf{x})+\epsilon$ with $\mathbf{x}\in \mathbb{R}^D$ and $D$ can be as large as few 100. I have attempted with the GPML toolbox and the exact ...
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Using a trained classifier in an Android app

As the title suggests, I'm attempting to train some different classifiers into an android app. The main question I have is how to represent the different models in a neat and effective way, from ...
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Advice on machine learning for small inputs and outputs

I am planning on using a machine learning algorithm to learn the mapping between sets of four coordinates (x,y,z + a distance d ...
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normalization/standardization of input/output of autoencoder and Gaussian Process

I have two machine learning algorithms that deal with time series data. My data consist of 1500 time series, each of 500 time components. The first machine learning algorithm is an autoencoder, ...
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What is a “variable index” in the Gaussian perspective?

I was going through this article about Gaussian processes, in which the author explains about the "variable index" in the form of a plot while writing about 2D Gaussian. The explanation and plot are ...
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Is it possible to train probabilistic model to return several distributions?

I have nonlinear data of function y(x), which is let's say parabolic. At some points of x there are several y's (look at the picture). Is it possible to train a probabilistic model to return several ...
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Fitting surface using Gaussian Processes (GPs) enclosing 3D cloud

I have a 3D cloud of data points representing an arbitrary shaped volume. I want to fit a GP to the outer surface of this 3d cloud. I saw many examples for interpolation using GPs. They only speak ...
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How exactly do Gaussian Processes (square dist kernel) enforce smoothness? (Aka how are they computed to do so?)

From: http://www.cs.cmu.edu/~16831-f12/notes/F10/16831_lecture22_jlisee/16831_lecture22.jlisee.pdf "Gaussian Processes artificially introduce correlation between close samples in that vector in order ...
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Model a regressor using GP

I was reading a paper and I don't quite understand the following sentence: In order to create a smoother landscape to perform optimization, we used a Gaussian process model to model the property ...
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Has anybody used alternative hyperparameter optimization techniques (other than default one) in SK-Learn?

I've been using Sklearn for Gaussian process regression that has L-BFGS-B (“fmin_l_bfgs_b”) as a default optimization algorithm. I want to implement some other ...
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How to fit a Gaussian Process with a product kernel K(x,a)=K_1(x,x')K_2(a,a') with scikit-learn?

I have a training dataset in the form of $(x, a, y)$ where $x$ and $a$ are two arrays of features and $y$ is the target outcome. I am interested in fitting a Gaussian Process with a product kernel $...
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How to combine different kernels for Gaussian process in GPyTorch?

I am trying to learn gaussian process by using GPyTorch to fit a Gaussian Process Regression model. However, I can't figure out ...