AI Article Synopsis
- Dreaming may play a crucial role in learning by helping to improve generative models, as many scientists suggest.
- Erik Hoel introduces a unique hypothesis that dreaming helps reduce overfitting, which is when a model is too tailored to its training data and fails to generalize well.
- This idea has implications for both neuroscience and advancements in machine-learning systems, highlighting the interdisciplinary nature of studying dreams.
Article Abstract
What is the purpose of dreaming? Many scientists have postulated a role for dreaming in learning, often with the aim of improving generative models. In this issue of , Erik Hoel proposes a novel hypothesis, namely, that dreaming provides a means to reduce overfitting. This hypothesis is interesting both for neuroscience and for the development of new machine-learning systems.
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Source |
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8134936 | PMC |
| http://dx.doi.org/10.1016/j.patter.2021.100268 | DOI Listing |
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