1 citations · 1 across the 3 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…
PreviewDiff: Multimodal Critic-Guided Search over Diffusion Latents
Vighnesh Subramaniam, Boris Katz, Brian Cheung +3
Diffusion models can produce striking images and videos, but they still struggle with the compositional details that make a generation faithful to a prompt, such as object counts,…
Network of Theseus (like the ship)
Vighnesh Subramaniam, Colin Conwell, Boris Katz +2
A standard assumption in deep learning is that the inductive bias introduced by a neural network architecture must persist from training through inference. The architecture you tra…
What if Eye...? Computationally Recreating Vision Evolution
Kushagra Tiwary, Aaron Young, Zaid Tasneem +6
Vision systems in nature show remarkable diversity, from simple light-sensitive patches to complex camera eyes with lenses. While natural selection has produced these eyes through…
How Biological Synapses Self-Assemble Gradient Learning
Qianli Liao, Liu Ziyin, Yulu Gan +4
Existing models of learning in the brain explain how given circuits learn, but not how biology could assemble those circuits in the first place. To address this gap, we formulate S…
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…