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Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

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Published on: May 31, 2017

Downward-Growing Neural Networks.

Vincenzo Laveglia¹, Edmondo Trentin²

¹DINFO, Università di Firenze, Via di S. Marta 3, 50139 Firenze, Italy.

Entropy (Basel, Switzerland)

|May 27, 2023

Summary

This summary is machine-generated.

This study introduces a downward-growing neural network (DGNN) to optimize deep learning architectures. DGNN improves model accuracy by intelligently growing networks, preventing overfitting and enhancing learning capabilities.

Keywords:

adaptive architecture deep learning deep neural network growing neural network target propagation

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Area of Science:

Artificial Intelligence
Machine Learning
Deep Learning

Background:

Defining optimal deep learning architectures is challenging, balancing model size to prevent overfitting or underfitting.
Automated architecture growing and pruning algorithms have been developed to address this challenge.

Purpose of the Study:

To introduce a novel approach for automatically growing deep neural network architectures, termed downward-growing neural network (DGNN).
To enhance the learning and generalization capabilities of deep neural networks by optimizing their architecture during training.

Main Methods:

DGNN applies to feed-forward deep neural networks.
Identifies and grows groups of neurons negatively impacting performance using ad hoc target propagation.
Simultaneously increases network depth and width.

Main Results:

DGNN demonstrated significant improvements in average accuracy on UCI datasets.
Outperformed established deep neural network approaches and existing growing algorithms like AdaNet and cascade correlation neural networks.

Conclusions:

DGNN offers an effective method for optimizing deep neural network architectures.
The approach successfully enhances model performance and generalization capabilities.