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Related Experiment Video

Updated: Jun 11, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Marginalized neural network mixtures for large-scale regression.

Miguel Lazaro-Gredilla¹, Aníbal R Figueiras-Vidal

¹Department of Signal Processing and Communications, Universidad Carlos III de Madrid, Madrid, Spain. miguel@tsc.uc3m.es

IEEE Transactions on Neural Networks

|July 6, 2010

Summary

This study introduces a novel mixture of neural networks (NNs) that offers probabilistic predictions and outperforms sparse Gaussian processes on large datasets, matching their computational efficiency. This approach enhances machine learning for regression tasks with big data.

Related Experiment Videos

Last Updated: Jun 11, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

Machine Learning
Probabilistic Modeling
Computational Statistics

Background:

Traditional neural networks (NNs) are often outperformed by Gaussian processes (GPs) for regression, offering probabilistic predictions and reduced overfitting.
The high computational cost of GPs necessitates sparse approximations for large datasets.
Sparse Gaussian processes aim to balance performance and computational efficiency.

Purpose of the Study:

To introduce a novel machine learning model combining neural networks and Gaussian processes.
To achieve probabilistic predictions and improved performance over sparse Gaussian processes.
To maintain computational efficiency comparable to sparse methods for large-scale regression tasks.

Main Methods:

Developed a mixture of neural networks (NNs) with marginalized output weights.
Integrated probabilistic prediction capabilities into the NN framework.
Evaluated the model's performance against sparse Gaussian processes on large datasets.

Main Results:

The proposed mixture of NNs provides probabilistic predictions, similar to Gaussian processes.
The model demonstrates superior performance compared to sparse Gaussian processes.
Achieved comparable computational cost to sparse Gaussian processes on large datasets.

Conclusions:

The novel mixture of NNs offers a powerful alternative for large-scale regression tasks.
This approach effectively combines the strengths of neural networks and Gaussian processes.
The method provides accurate probabilistic predictions with efficient computation.