8 citations · 43 across the 12 of their papers we have counts for
7 papers · 1 filter
Flexible social inference facilitates targeted social learning when rewards are not observable
Robert D. Hawkins, Andrew M. Berdahl, Alex "Sandy" Pentland +3
Groups coordinate more effectively when individuals are able to learn from others' successes. But acquiring such knowledge is not always easy, especially in real-world environments…
Abstract Visual Reasoning with Tangram Shapes
Anya Ji, Noriyuki Kojima, Noah Rush +4
We introduce KiloGram, a resource for studying abstract visual reasoning in humans and machines. Drawing on the history of tangram puzzles as stimuli in cognitive science, we build…
How to talk so AI will learn: Instructions, descriptions, and autonomy
Theodore R Sumers, Robert D Hawkins, Mark K Ho +2
From the earliest years of our lives, humans use language to express our beliefs and desires. Being able to talk to artificial agents about our preferences would thus fulfill a cen…
Identifying concept libraries from language about object structure
Catherine Wong, William P. McCarthy, Gabriel Grand +5
Our understanding of the visual world goes beyond naming objects, encompassing our ability to parse objects into meaningful parts, attributes, and relations. In this work, we lever…
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines
Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta +7
Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks. Although meta-learning is a method to endow neural networks with useful inductive bi…
Mixed-effects transformers for hierarchical adaptation
Julia White, Noah Goodman, Robert Hawkins
Language use differs dramatically from context to context. To some degree, modern language models like GPT-3 are able to account for such variance by conditioning on a string of pr…