35 citations · 54 across the 9 of their papers we have counts for
9 papers
Investigating Concept Alignment Using Implausible Category Members
Sunayana Rane, Brenden M. Lake, Thomas L. Griffiths
Developing AI systems with a human-like understanding of everyday concepts is a key step towards developing safe, reliable systems whose behavior makes sense to humans. When probin…
Convolutional Neural Networks Can (Meta-)Learn the Same-Different Relation
Max Gupta, Sunayana Rane, R. Thomas McCoy +1
While convolutional neural networks (CNNs) have come to match and exceed human performance in many settings, the tasks these models optimize for are largely constrained to the leve…
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…
The Reasonable Person Standard for AI
Sunayana Rane
As AI systems are increasingly incorporated into domains where human behavior has set the norm, a challenge for AI governance and AI alignment research is to regulate their behavio…
DOCCI: Descriptions of Connected and Contrasting Images
Yasumasa Onoe, Sunayana Rane, Zachary Berger +9
Vision-language datasets are vital for both text-to-image (T2I) and image-to-text (I2T) research. However, current datasets lack descriptions with fine-grained detail that would al…
Concept Alignment
Sunayana Rane, Polyphony J. Bruna, Ilia Sucholutsky +2
Discussion of AI alignment (alignment between humans and AI systems) has focused on value alignment, broadly referring to creating AI systems that share human values. We argue that…
Concept Alignment as a Prerequisite for Value Alignment
Sunayana Rane, Mark Ho, Ilia Sucholutsky +1
Value alignment is essential for building AI systems that can safely and reliably interact with people. However, what a person values -- and is even capable of valuing -- depends o…
Getting aligned on representational alignment
Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller +30
Biological and artificial information processing systems form representations of the world that they can use to categorize, reason, plan, navigate, and make decisions. How can we m…
Predicting Word Learning in Children from the Performance of Computer Vision Systems
Sunayana Rane, Mira L. Nencheva, Zeyu Wang +3
For human children as well as machine learning systems, a key challenge in learning a word is linking the word to the visual phenomena it describes. We explore this aspect of word…