11 citations · 32 across the 8 of their papers we have counts for
10 papers
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…
H-ARC: A Robust Estimate of Human Performance on the Abstraction and Reasoning Corpus Benchmark
Solim LeGris, Wai Keen Vong, Brenden M. Lake +1
The Abstraction and Reasoning Corpus (ARC) is a visual program synthesis benchmark designed to test challenging out-of-distribution generalization in humans and machines. Since 201…
Beyond the Doors of Perception: Vision Transformers Represent Relations Between Objects
Michael A. Lepori, Alexa R. Tartaglini, Wai Keen Vong +3
Though vision transformers (ViTs) have achieved state-of-the-art performance in a variety of settings, they exhibit surprising failures when performing tasks involving visual relat…
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…
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…
A Developmentally-Inspired Examination of Shape versus Texture Bias in Machines
Alexa R. Tartaglini, Wai Keen Vong, Brenden M. Lake
Early in development, children learn to extend novel category labels to objects with the same shape, a phenomenon known as the shape bias. Inspired by these findings, Geirhos et al…
Fast and flexible: Human program induction in abstract reasoning tasks
Aysja Johnson, Wai Keen Vong, Brenden M. Lake +1
The Abstraction and Reasoning Corpus (ARC) is a challenging program induction dataset that was recently proposed by Chollet (2019). Here, we report the first set of results collect…
Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching
Scott Cheng-Hsin Yang, Wai Keen Vong, Ravi B. Sojitra +2
State-of-the-art deep-learning systems use decision rules that are challenging for humans to model. Explainable AI (XAI) attempts to improve human understanding but rarely accounts…
Learning word-referent mappings and concepts from raw inputs
Wai Keen Vong, Brenden M. Lake
How do children learn correspondences between the language and the world from noisy, ambiguous, naturalistic input? One hypothesis is via cross-situational learning: tracking words…
Optimal Cooperative Inference
Scott Cheng-Hsin Yang, Yue Yu, Arash Givchi +3
Cooperative transmission of data fosters rapid accumulation of knowledge by efficiently combining experiences across learners. Although well studied in human learning and increasin…