Latent learning: How episodic memory could improve machine learning generalization
By
Andrew Kyle Lampinen, Martin Engelcke, Yuxuan Li, Arslan Chaudhry, James McClelland
Pale and squishy. Not ruined, just not done.
Summary
This article examines why machine learning systems fail to generalize, drawing inspiration from cognitive science. It argues that parametric ML systems lack "latent learning"—the ability to absorb information not immediately relevant to the current task but potentially useful for future tasks. Using synthetic benchmarks, the research connects failures like the reversal curse in language modeling to new findings in agent-based navigation, suggesting that incorporating episodic memory mechanisms could improve generalization in ML systems.
Key quotes
· 3 pulledone weakness of parametric machine learning systems is their failure to exhibit latent learning---learning information that is not relevant to the task at hand, but that might be useful in a future task
we draw inspiration from cognitive science to argue that one weakness of...
we show how this perspective links failures ranging from the reversal curse in language modeling to new findings on agent-based navigation
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