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Found 7 papers, 2 papers with code
Deep Neural Networks Can Learn Generalizable Same-Different Visual Relations
Much of this prior work focuses on training convolutional neural networks to classify images of two same or two different abstract shapes, testing generalization on within-distribution stimuli.
Abstract Visual Reasoning with Tangram Shapes
We introduce KiloGram, a resource for studying abstract visual reasoning in humans and machines.
A Developmentally-Inspired Examination of Shape versus Texture Bias in Machines
In this work, we re-examine the inductive biases of neural networks by adapting the stimuli and procedure from Geirhos et al. (2019) to more closely follow the developmental paradigm and test on a wide range of pre-trained neural networks.
Fast and flexible: Human program induction in abstract reasoning tasks
The Abstraction and Reasoning Corpus (ARC) is a challenging program induction dataset that was recently proposed by Chollet (2019).
Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching
State-of-the-art deep-learning systems use decision rules that are challenging for humans to model.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Learning word-referent mappings and concepts from raw inputs
How do children learn correspondences between the language and the world from noisy, ambiguous, naturalistic input?
Optimal Cooperative Inference
Cooperative transmission of data fosters rapid accumulation of knowledge by efficiently combining experiences across learners.
