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Related Experiment Video

Updated: Oct 21, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Extremely randomized neural networks for constructing prediction intervals.

Tullio Mancini¹, Hector Calvo-Pardo², Jose Olmo³

¹University of Southampton, United Kingdom.

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

|September 6, 2021

Summary

This study introduces a new deep neural network ensemble model that enhances prediction accuracy and uncertainty estimation. The novel method improves upon existing techniques, offering better performance in various settings.

Keywords:

Dropout Ensemble methods Neural networks Prediction interval Uncertainty quantification

Related Experiment Videos

Last Updated: Oct 21, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

Machine Learning
Statistical Modeling
Artificial Intelligence

Background:

Traditional prediction models often struggle with variance and out-of-sample accuracy.
Bootstrap-based algorithms have limitations, especially in low-dimensional or non-i.i.d. settings.

Purpose of the Study:

To propose a novel prediction model using an ensemble of deep neural networks.
To adapt the extremely randomized trees method for improved prediction and uncertainty quantification.
To overcome limitations of existing resampling-based algorithms.

Main Methods:

Ensemble of deep neural networks.
Adaptation of the extremely randomized trees algorithm.
No data resampling, suitable for low/mid-dimensional and non-i.i.d. data.

Main Results:

Reduced prediction variance and improved out-of-sample accuracy.
Capability to compute prediction uncertainty and construct interval forecasts.
Superior performance compared to MC dropout and bootstrap procedures in simulations.

Conclusions:

The novel ensemble deep neural network model offers enhanced prediction accuracy and reliable uncertainty estimation.
The method is robust in various data settings, including those where the i.i.d. assumption does not hold.
This approach represents an advancement over current state-of-the-art prediction techniques.