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Augmenting Machine Learning with Flexible Episodic Memory

Hugo Chateau-Laurent 1 Frédéric Alexandre 1 
1 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives [Bordeaux]
Abstract : A major cognitive function is often overlooked in artificial intelligence research: episodic memory. In this paper, we relate episodic memory to the more general need for explicit memory in intelligent processing. We describe its main mechanisms and its involvement in a variety of functions, ranging from concept learning to planning. We set the basis for a computational cognitive neuroscience approach that could result in improved machine learning models. More precisely, we argue that episodic memory mechanisms are crucial for contextual decision making, generalization through consolidation and prospective memory.
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https://hal.inria.fr/hal-03359384
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Submitted on : Thursday, September 30, 2021 - 10:42:23 AM
Last modification on : Sunday, June 26, 2022 - 3:14:34 AM
Long-term archiving on: : Friday, December 31, 2021 - 6:27:59 PM

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  • HAL Id : hal-03359384, version 1

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Hugo Chateau-Laurent, Frédéric Alexandre. Augmenting Machine Learning with Flexible Episodic Memory. 13th International Joint Conference on Computational Intelligence, Oct 2021, Valletta, Malta. ⟨hal-03359384⟩

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