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Model Reduction Through Progressive Latent Space Pruning in Deep Active Inference
Although still not fully understood, sleep is known to play an important role in learning and in pruning synaptic connections. From the active inference perspective, this can be cast as learning parameters of a generative model and Bayesian model reduction, respectively. In this article, we show how...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8961807/ https://www.ncbi.nlm.nih.gov/pubmed/35360827 http://dx.doi.org/10.3389/fnbot.2022.795846 |
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author | Wauthier, Samuel T. De Boom, Cedric Çatal, Ozan Verbelen, Tim Dhoedt, Bart |
author_facet | Wauthier, Samuel T. De Boom, Cedric Çatal, Ozan Verbelen, Tim Dhoedt, Bart |
author_sort | Wauthier, Samuel T. |
collection | PubMed |
description | Although still not fully understood, sleep is known to play an important role in learning and in pruning synaptic connections. From the active inference perspective, this can be cast as learning parameters of a generative model and Bayesian model reduction, respectively. In this article, we show how to reduce dimensionality of the latent space of such a generative model, and hence model complexity, in deep active inference during training through a similar process. While deep active inference uses deep neural networks for state space construction, an issue remains in that the dimensionality of the latent space must be specified beforehand. We investigate two methods that are able to prune the latent space of deep active inference models. The first approach functions similar to sleep and performs model reduction post hoc. The second approach is a novel method which is more similar to reflection, operates during training and displays “aha” moments when the model is able to reduce latent space dimensionality. We show for two well-known simulated environments that model performance is retained in the first approach and only diminishes slightly in the second approach. We also show that reconstructions from a real world example are indistinguishable before and after reduction. We conclude that the most important difference constitutes a trade-off between training time and model performance in terms of accuracy and the ability to generalize, via minimization of model complexity. |
format | Online Article Text |
id | pubmed-8961807 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89618072022-03-30 Model Reduction Through Progressive Latent Space Pruning in Deep Active Inference Wauthier, Samuel T. De Boom, Cedric Çatal, Ozan Verbelen, Tim Dhoedt, Bart Front Neurorobot Neuroscience Although still not fully understood, sleep is known to play an important role in learning and in pruning synaptic connections. From the active inference perspective, this can be cast as learning parameters of a generative model and Bayesian model reduction, respectively. In this article, we show how to reduce dimensionality of the latent space of such a generative model, and hence model complexity, in deep active inference during training through a similar process. While deep active inference uses deep neural networks for state space construction, an issue remains in that the dimensionality of the latent space must be specified beforehand. We investigate two methods that are able to prune the latent space of deep active inference models. The first approach functions similar to sleep and performs model reduction post hoc. The second approach is a novel method which is more similar to reflection, operates during training and displays “aha” moments when the model is able to reduce latent space dimensionality. We show for two well-known simulated environments that model performance is retained in the first approach and only diminishes slightly in the second approach. We also show that reconstructions from a real world example are indistinguishable before and after reduction. We conclude that the most important difference constitutes a trade-off between training time and model performance in terms of accuracy and the ability to generalize, via minimization of model complexity. Frontiers Media S.A. 2022-03-11 /pmc/articles/PMC8961807/ /pubmed/35360827 http://dx.doi.org/10.3389/fnbot.2022.795846 Text en Copyright © 2022 Wauthier, De Boom, Çatal, Verbelen and Dhoedt. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Wauthier, Samuel T. De Boom, Cedric Çatal, Ozan Verbelen, Tim Dhoedt, Bart Model Reduction Through Progressive Latent Space Pruning in Deep Active Inference |
title | Model Reduction Through Progressive Latent Space Pruning in Deep Active Inference |
title_full | Model Reduction Through Progressive Latent Space Pruning in Deep Active Inference |
title_fullStr | Model Reduction Through Progressive Latent Space Pruning in Deep Active Inference |
title_full_unstemmed | Model Reduction Through Progressive Latent Space Pruning in Deep Active Inference |
title_short | Model Reduction Through Progressive Latent Space Pruning in Deep Active Inference |
title_sort | model reduction through progressive latent space pruning in deep active inference |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8961807/ https://www.ncbi.nlm.nih.gov/pubmed/35360827 http://dx.doi.org/10.3389/fnbot.2022.795846 |
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