9 papers
Continual Visual and Verbal Learning Through a Child's Egocentric Input
Xiaoyang Jiang, Yanlai Yang, Kenneth A. Norman +2
Children learn the meanings of words from a continuous, temporally structured stream of egocentric experience. Recent work shows that neural networks can also learn word-referent m…
On the robustness of modeling grounded word learning through a child's egocentric input
Wai Keen Vong, Brenden M. Lake
What insights can machine learning bring to understanding human language acquisition? Large language and multimodal models have achieved remarkable capabilities, but their reliance…
Do different prompting methods yield a common task representation in language models?
Guy Davidson, Todd M. Gureckis, Brenden M. Lake +1
Demonstrations and instructions are two primary approaches for prompting language models to perform in-context learning (ICL) tasks. Do identical tasks elicited in different ways r…
Overcoming classic challenges for artificial neural networks by providing incentives and practice
Kazuki Irie, Brenden M. Lake
Since the earliest proposals for artificial neural network (ANN) models of the mind and brain, critics have pointed out key weaknesses in these models compared to human cognitive a…
Deep Neural Networks Can Learn Generalizable Same-Different Visual Relations
Alexa R. Tartaglini, Sheridan Feucht, Michael A. Lepori +4
Although deep neural networks can achieve human-level performance on many object recognition benchmarks, prior work suggests that these same models fail to learn simple abstract re…
Do Large Language Models Reason Causally Like Us? Even Better?
Hanna M. Dettki, Brenden M. Lake, Charley M. Wu +1
Causal reasoning is a core component of intelligence. Large language models (LLMs) have shown impressive capabilities in generating human-like text, raising questions about whether…