A striking example of spatial reasoning is humans’ ability to recognize objects across viewpoints—a skill known as mental rotation.
How does the brain achieve this ability? To answer this, we propose a mechanistic model of human mental rotation.
Paper: arxiv.org/abs/2512.13517
1/🧵
How does the brain achieve this ability? To answer this, we propose a mechanistic model of human mental rotation.
Paper: arxiv.org/abs/2512.13517
1/🧵
December 18, 2025 at 9:46 AM
A striking example of spatial reasoning is humans’ ability to recognize objects across viewpoints—a skill known as mental rotation.
How does the brain achieve this ability? To answer this, we propose a mechanistic model of human mental rotation.
Paper: arxiv.org/abs/2512.13517
1/🧵
How does the brain achieve this ability? To answer this, we propose a mechanistic model of human mental rotation.
Paper: arxiv.org/abs/2512.13517
1/🧵
Reposted by Ray
Little is known about how deep networks interact with structure in data. An important aspect of this structure is symmetry (e.g., pose transformations). Here, we (w/ @stphtphsn.bsky.social) study the generalization ability of deep networks on symmetric datasets: arxiv.org/abs/2412.11521 (1/n)
On the Ability of Deep Networks to Learn Symmetries from Data: A Neural Kernel Theory
Symmetries (transformations by group actions) are present in many datasets, and leveraging them holds significant promise for improving predictions in machine learning. In this work, we aim to underst...
arxiv.org
January 14, 2025 at 1:05 PM
Little is known about how deep networks interact with structure in data. An important aspect of this structure is symmetry (e.g., pose transformations). Here, we (w/ @stphtphsn.bsky.social) study the generalization ability of deep networks on symmetric datasets: arxiv.org/abs/2412.11521 (1/n)