1 citations · 2 across the 4 of their papers we have counts for
6 papers
ArtifactArena: Evaluating Models by What They Build in the Physical World
Kushagra Tiwary, Kushagra Tiwary*, David Mayo* +7
To evaluate the frontier, we must measure models not by what they say, but by what they can engineer and build in grounded physical environments. We introduce ArtifactArena, an ope…
BrainBits: How Much of the Brain are Generative Reconstruction Methods Using?
David Mayo, Christopher Wang, Asa Harbin +4
When evaluating stimuli reconstruction results it is tempting to assume that higher fidelity text and image generation is due to an improved understanding of the brain or more powe…
Using Multimodal Deep Neural Networks to Disentangle Language from Visual Aesthetics
Colin Conwell, Christopher Hamblin, Chelsea Boccagno +4
When we experience a visual stimulus as beautiful, how much of that experience derives from perceptual computations we cannot describe versus conceptual knowledge we can readily tr…
Training the Untrainable: Introducing Inductive Bias via Representational Alignment
Vighnesh Subramaniam, David Mayo, Colin Conwell +4
We demonstrate that architectures which traditionally are considered to be ill-suited for a task can be trained using inductive biases from another architecture. We call a network…
Developing a Series of AI Challenges for the United States Department of the Air Force
Vijay Gadepally, Gregory Angelides, Andrei Barbu +39
Through a series of federal initiatives and orders, the U.S. Government has been making a concerted effort to ensure American leadership in AI. These broad strategy documents have…
On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation
Binxu Wang, David Mayo, Arturo Deza +2
Self-supervised learning is a powerful way to learn useful representations from natural data. It has also been suggested as one possible means of building visual representation in…