Questions tagged [gaussian-process]

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Sequential sampling from Gaussian conditional not working

I'm trying to sequentially sample from a Gaussian Process prior. The problem is that the samples eventually converge to zero or diverge to infinity. I'm using the basic conditionals described e.g. ...
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1answer
32 views

Data model with more outputs than inputs?

I am working on parametric studies in physics simulations, i.e. I vary some real input parameters (e.g. x0,x1,x2,x3) and get an output with a larger size (e.g. y0,y1 ... y100). Assuming that I have a ...
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42 views

Gaussian Process Classifier and specifying kernel

I am using scikitlearn's gaussian process classifier and either I don't think I understand how the kernel is used (more likely), or there is an error in the module (less likely). In short, the ...
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17 views

How do I create these Kernel functions in Python for Gaussian Process Regression?

I have a dataset of 1031 observed samples of 7 features that form the X and one target variable that forms the Y. I am using Gaussian Process Regressor to train my models. I want to use anisotropic ...
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16 views

Derivative of multi-output Gaussian Process

I am working on a project where I estimate transition and measurements models for a kalman filter using Gaussian Processes. In order to linearize the models I require the Jacobian of the estimated ...
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9 views

Fuzzy K-Means: Can I scale training targets by Membership Coefficients?

I am attempting to improve on a method which predicts heterogeneous values based on their Cartesian coordinates (x and y). The current method first clusters the training set values, then trains a GPR ...
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26 views

GP derivative in GpyTorch

I am working on a project using GP-regression models to model transition and measurements models in a Kalman Filter. This means I need to be able to sample from the derivative of the original GP model....
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1answer
32 views

Hyperparameter tuning of neural networks using Bayesian Optimization

One of the assumptions for finding good hyperparameters using Bayesian optimization (GP) is that the unknown function is smooth. Is this assumption valid for neural networks or at least for most of ...
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22 views

Gaussian process regressor returns almost identical std for all datapoints

I am using a Gaussian process regressor as the regressor for active learning and I use its standard deviation to choose the next training inctance (the one with the highest std is chosen). However, ...
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1answer
21 views

Gaussian Process for Classification: How to do predictions using MCMC methods

Problem I was reading about Gaussian Processes for regression in the "Gaussian Processes for Classification" textbook and in a few other online resources. Everywhere I look people seem to avoid ...
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23 views

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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182 views

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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1answer
30 views

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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84 views

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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1answer
34 views

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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2answers
39 views

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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2answers
37 views

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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102 views

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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1answer
502 views

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 ...