75 citations · 326 across the 20 of their papers we have counts for
4 papers · 1 filter
Tell me why! Explanations support learning relational and causal structure
Andrew K. Lampinen, Nicholas A. Roy, Ishita Dasgupta +8
Inferring the abstract relational and causal structure of the world is a major challenge for reinforcement-learning (RL) agents. For humans, language--particularly in the form of e…
Distinguishing rule- and exemplar-based generalization in learning systems
Ishita Dasgupta, Erin Grant, Thomas L. Griffiths
Machine learning systems often do not share the same inductive biases as humans and, as a result, extrapolate or generalize in ways that are inconsistent with our expectations. The…
Passive Attention in Artificial Neural Networks Predicts Human Visual Selectivity
Thomas A. Langlois, H. Charles Zhao, Erin Grant +3
Developments in machine learning interpretability techniques over the past decade have provided new tools to observe the image regions that are most informative for classification…
Are Convolutional Neural Networks or Transformers more like human vision?
Shikhar Tuli, Ishita Dasgupta, Erin Grant +1
Modern machine learning models for computer vision exceed humans in accuracy on specific visual recognition tasks, notably on datasets like ImageNet. However, high accuracy can be…