For Human-Like Models, Train on Human-Like Tasks
Copyright policy )NSF| CompCog: Bridging Levels of Analysis: Characterizing Algorithmic Models by Extreme Bayesian Priors
Katherine Hermann; Aran Nayebi; Sjoerd van Steenkiste; Matt Jones;
For Human-Like Models, Train on Human-Like Tasks
Bowers et al. (2022) express skepticism about deep neural networks (DNNs) as models of human vision due to DNNs’ failures to account for results from psychological research. We argue that to fairly assess DNNs, we must first train them on more human-like tasks which we hypothesize will induce more human-like behaviors and representations.
- University of Colorado Boulder United States
- Massachusetts Institute of Technology United States
- University of Colorado System United States
- University of Colorado Health United States
- Google (United States) United States
Deep Learning, Humans, Neural Networks, Computer
Deep Learning, Humans, Neural Networks, Computer
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selected citations
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These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP! 0
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