3 papers
cs.LG2026
Principles and Practice of Deep Representation Learning: or a Mathematical Theory of Memory
Sam Buchanan, Druv Pai, Peng Wang +1
In the current era of deep learning and especially generative models, there is significant investment in training very large deep neural networks. Thus far, such models have been "…
cs.LG2025
On the Edge of Memorization in Diffusion Models
Sam Buchanan, Druv Pai, Yi Ma +1
When do diffusion models reproduce their training data, and when are they able to generate samples beyond it? A practically relevant theoretical understanding of this interplay bet…
cs.CV2024
Canonical Factors for Hybrid Neural Fields
Brent Yi, Weijia Zeng, Sam Buchanan +1
Factored feature volumes offer a simple way to build more compact, efficient, and intepretable neural fields, but also introduce biases that are not necessarily beneficial for real…