The remarkable successes of convolutional neural networks (CNNs) in modern computer
vision are by now well known, and they are increasingly being explored as computational …

Understanding the neural basis of the remarkable human cognitive capacity to learn novel
concepts from just one or a few sensory experiences constitutes a fundamental problem. We …

Categorization and old-new recognition memory are closely linked topics in the cognitive-
psychology literature and there have been extensive past efforts at develo** unified formal …

Quantitative, data-driven models for mental representations have long enjoyed popularity
and success in psychology (eg, distributional semantic models in the language domain), but …

Supervised learning typically focuses on learning transferable representations from training
examples annotated by humans. While rich annotations (like soft labels) carry more …

Different speakers often use different names to refer to the same entity (eg,“woman”
vs.“tennis player” for a given woman playing tennis). We study how visual typicality affects …

We study the robustness of conformal prediction—a powerful tool for uncertainty
quantification—to label noise. Our analysis tackles both regression and classification …

In this paper, we introduce an AI-mediated framework that can provide intelligent feedback
to augment human cognition. Specifically, we leverage deep reinforcement learning (DRL) …

Convolutional neural networks (CNNs) are increasingly widely used in psychology and
neuroscience to predict how human minds and brains respond to visual images. Typically …

A core problem in visual object learning is using a finite number of images of a new object to
accurately identify that object in future, novel images. One longstanding, conceptual …