Work

Meaningful Representations Prevent Catastrophic Interference

Year: 2009

Type: conference-paper

Abstract: Artificial Neural Networks (ANNs) attempt to mimic human neural networks in order to perform tasks. In order to do this, tasks need to be represented in ways that the network understands. In ANNs these representations are often arbitrary, whereas in humans it seems that these representations are often meaningful. This article shows how using more meaningful representations in ANNs can be very beneficial. We demonstrate that by using our Static Meaningful Representation Learning (SMRL) technique, ANNs can avoid the problem of catastrophic interference when sequentially learning multiple simple tasks. We also discuss how our approach overcomes known limitations of other techniques for dealing with catastrophic interference.

Source: Radboud Repository (Radboud University)

Authors: Jordi Bieger, I.G. Sprinkhuizen-Kuyper, I.J.E.I. van Rooij

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W1583088546

FWCI:

Cites: 27

Cited by:

Related to: 10