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Related Experiment Video

Updated: Nov 4, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

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A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multioutput Gaussian Process Model.

Juan-Jose Giraldo, Mauricio A Alvarez

    IEEE Transactions on Neural Networks and Learning Systems
    |May 24, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel optimization scheme for multioutput Gaussian processes, enhancing performance for heterogeneous data. The natural gradient optimization improves results over adaptive methods for both LMC and convolution models.

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    Last Updated: Nov 4, 2025

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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    Area of Science:

    • Machine Learning
    • Statistical Modeling
    • Computational Statistics

    Background:

    • Multioutput Gaussian processes (GPs) are extended to model heterogeneous outputs using individual likelihood functions.
    • A vector-valued GP prior with a linear model of coregionalization (LMC) covariance jointly models likelihood parameters as latent functions.
    • Stochastic variational inference (SVI) is enabled via an inducing points' framework, yielding tractable variational bounds.

    Purpose of the Study:

    • To address optimization challenges in heterogeneous multioutput GP models, specifically the conditioning issues with adaptive gradient methods.
    • To introduce a novel natural gradient (NG) optimization scheme using an exploratory distribution over hyperparameters.
    • To extend the heterogeneous multioutput model with latent functions from convolution processes and optimize it using NG.

    Main Methods:

    • Developed a natural gradient (NG) optimization scheme by introducing an exploratory distribution over hyperparameters for joint inference.
    • Extended the heterogeneous multioutput GP model to incorporate convolution processes for latent functions.
    • Applied SVI to the convolutional model for scalability and optimized it using the proposed NG scheme.

    Main Results:

    • The NG optimization scheme achieved superior local optima and higher test performance rates compared to adaptive gradient methods for both LMC and convolution process models.
    • Demonstrated the scalability of the convolutional GP model through SVI.
    • Showcased the effectiveness of the NG optimization for the scalable convolutional model.

    Conclusions:

    • The proposed natural gradient optimization scheme significantly improves performance and stability for heterogeneous multioutput Gaussian processes.
    • Convolution processes offer a flexible alternative for latent function modeling in heterogeneous multioutput GPs.
    • The developed methods provide efficient and scalable solutions for complex multioutput GP modeling and inference.