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Found 6 papers, 0 papers with code
Exploiting Attention to Reveal Shortcomings in Memory Models
The decision making processes of deep networks are difficult to understand and while their accuracy often improves with increased architectural complexity, so too does their opacity.
Capturing Human Category Representations by Sampling in Deep Feature Spaces
In this paper, we introduce a method for estimating the structure of human categories that draws on ideas from both cognitive science and machine learning, blending human-based algorithms with state-of-the-art deep representation learners.
A graph-theoretic approach to multitasking
A key feature of neural network architectures is their ability to support the simultaneous interaction among large numbers of units in the learning and processing of representations.
Algorithm selection by rational metareasoning as a model of human strategy selection
Rational metareasoning appears to be a promising framework for reverse-engineering how people choose among cognitive strategies and translating the results into better solutions to the algorithm selection problem.
Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies
Learning a visual concept from a small number of positive examples is a significant challenge for machine learning algorithms.
Burn-in, bias, and the rationality of anchoring
Therefore minds and machines have to approximate Bayesian inference.
