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Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

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Published on: September 25, 2021

Weighted contrastive divergence.

Enrique Romero¹, Ferran Mazzanti², Jordi Delgado¹

¹Departament de Ciències de la Computació, Universitat Politècnica de Catalunya - BarcelonaTech, Spain.

Neural Networks : the Official Journal of the International Neural Network Society

|March 29, 2019

Summary

This summary is machine-generated.

Weighted Contrastive Divergence (WCD) is a new algorithm for training Boltzmann machines. WCD improves upon standard Contrastive Divergence (CD) and persistent CD, offering better performance with minimal extra computation.

Keywords:

Contrastive divergence Neural networks Restricted Boltzmann machine

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Area of Science:

Machine Learning
Artificial Intelligence
Computational Neuroscience

Background:

Training energy-based Boltzmann architectures using gradient descent is computationally intensive.
Approximation schemes are necessary for gradient evaluation, leading to limitations in algorithms like Restricted Boltzmann Machines (RBM) and Contrastive Divergence (CD).
Existing algorithms like persistent CD have been developed to address CD's shortcomings.

Purpose of the Study:

To introduce a novel learning algorithm, Weighted Contrastive Divergence (WCD), for Boltzmann machines.
To enhance the efficiency and effectiveness of gradient-based learning in these architectures.
To provide a computationally feasible alternative to existing methods.

Main Methods:

The proposed Weighted Contrastive Divergence (WCD) algorithm modifies the negative phase of standard Contrastive Divergence (CD).
Experimental evaluations were conducted to compare WCD against standard CD and persistent CD.
The focus was on assessing performance improvements and computational overhead.

Main Results:

Weighted Contrastive Divergence (WCD) demonstrates significant improvements over standard CD and persistent CD.
These enhancements are achieved with only a marginal increase in computational cost.
Experimental results validate the effectiveness of the proposed modifications.

Conclusions:

Weighted Contrastive Divergence (WCD) offers a promising advancement in training Boltzmann machines.
The algorithm provides a superior balance of performance and computational efficiency.
WCD represents a valuable contribution to the field of machine learning for energy-based models.