328 citations · 562 across the 10 of their papers we have counts for
10 papers
Zero-Shot Object-Centric Representation Learning
Aniket Didolkar, Andrii Zadaianchuk, Anirudh Goyal +4
The goal of object-centric representation learning is to decompose visual scenes into a structured representation that isolates the entities. Recent successes have shown that objec…
Narrowing the Focus: Learned Optimizers for Pretrained Models
Gus Kristiansen, Mark Sandler, Andrey Zhmoginov +4
In modern deep learning, the models are learned by applying gradient updates using an optimizer, which transforms the updates based on various statistics. Optimizers are often hand…
Can AI Be as Creative as Humans?
Haonan Wang, James Zou, Michael Mozer +8
Creativity serves as a cornerstone for societal progress and innovation. With the rise of advanced generative AI models capable of tasks once reserved for human creativity, the stu…
Skill-Mix: a Flexible and Expandable Family of Evaluations for AI models
Dingli Yu, Simran Kaur, Arushi Gupta +3
With LLMs shifting their role from statistical modeling of language to serving as general-purpose AI agents, how should LLM evaluations change? Arguably, a key ability of an AI age…
Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior
Ayush Chakravarthy, Trang Nguyen, Anirudh Goyal +2
The aim of object-centric vision is to construct an explicit representation of the objects in a scene. This representation is obtained via a set of interchangeable modules called \…
DiscoGen: Learning to Discover Gene Regulatory Networks
Nan Rosemary Ke, Sara-Jane Dunn, Jorg Bornschein +11
Accurately inferring Gene Regulatory Networks (GRNs) is a critical and challenging task in biology. GRNs model the activatory and inhibitory interactions between genes and are inhe…