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Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners
To solve the machine teaching optimization problem, we use a deep learning approximation method which simulates learners in the environment and learns to predict how feedback affects the learner's internal states.
Showing versus doing: Teaching by demonstration
Stark differences arise when demonstrators are intentionally teaching a task versus simply performing a task.
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.
Human memory search as a random walk in a semantic network
The human mind has a remarkable ability to store a vast amount of information in memory, and an even more remarkable ability to retrieve these experiences when needed.
An ideal observer model for identifying the reference frame of objects
The object people perceive in an image can depend on its orientation relative to the scene it is in (its reference frame).
Learning invariant features using the Transformed Indian Buffet Process
Identifying the features of objects becomes a challenge when those features can change in their appearance.
Analyzing human feature learning as nonparametric Bayesian inference
Almost all successful machine learning algorithms and cognitive models require powerful representations capturing the features that are relevant to a particular problem.
