3 citations · 8 across the 5 of their papers we have counts for
5 papers
AI-enhanced semantic feature norms for 786 concepts
Siddharth Suresh, Kushin Mukherjee, Tyler Giallanza +4
Semantic feature norms have been foundational in the study of human conceptual knowledge, yet traditional methods face trade-offs between concept/feature coverage and verifiability…
Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem
Declan Campbell, Sunayana Rane, Tyler Giallanza +8
Recent work has documented striking heterogeneity in the performance of state-of-the-art vision language models (VLMs), including both multimodal language models and text-to-image…
Human-Like Geometric Abstraction in Large Pre-trained Neural Networks
Declan Campbell, Sreejan Kumar, Tyler Giallanza +2
Humans possess a remarkable capacity to recognize and manipulate abstract structure, which is especially apparent in the domain of geometry. Recent research in cognitive science su…
Relational Constraints On Neural Networks Reproduce Human Biases towards Abstract Geometric Regularity
Declan Campbell, Sreejan Kumar, Tyler Giallanza +2
Uniquely among primates, humans possess a remarkable capacity to recognize and manipulate abstract structure in the service of task goals across a broad range of behaviors. One ill…
Context Matters: Recovering Human Semantic Structure from Machine Learning Analysis of Large-Scale Text Corpora
Marius Cătălin Iordan, Tyler Giallanza, Cameron T. Ellis +2
Applying machine learning algorithms to large-scale, text-based corpora (embeddings) presents a unique opportunity to investigate at scale how human semantic knowledge is organized…