2 citations · 2 across the 6 of their papers we have counts for
6 papers
Is Your Conditional Diffusion Model Actually Denoising?
Daniel Pfrommer, Zehao Dou, Christopher Scarvelis +2
We study the inductive biases of diffusion models with a conditioning-variable, which have seen widespread application as both text-conditioned generative image models and observat…
Sensitivity Analysis for Diffusion Models
Christopher Scarvelis, Justin Solomon
Training a diffusion model approximates a map from a data distribution to the optimal score function for that distribution. Can we differentiate this map? If we could, th…
Nuclear Norm Regularization for Deep Learning
Christopher Scarvelis, Justin Solomon
Penalizing the nuclear norm of a function's Jacobian encourages it to locally behave like a low-rank linear map. Such functions vary locally along only a handful of directions, mak…
Closed-Form Diffusion Models
Christopher Scarvelis, Haitz Sáez de Ocáriz Borde, Justin Solomon
Score-based generative models (SGMs) sample from a target distribution by iteratively transforming noise using the score function of the perturbed target. For any finite training s…
Riemannian Metric Learning via Optimal Transport
Christopher Scarvelis, Justin Solomon
We introduce an optimal transport-based model for learning a metric tensor from cross-sectional samples of evolving probability measures on a common Riemannian manifold. We neurall…
Relative Position Prediction as Pre-training for Text Encoders
Rickard Brüel-Gabrielsson, Chris Scarvelis
Meaning is defined by the company it keeps. However, company is two-fold: It's based on the identity of tokens and also on their position (topology). We argue that a position-centr…