46 citations · 46 across the 4 of their papers we have counts for
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Learning Orthonormal Bases for Function Spaces
Hamidreza Kamkari, Mohammad Sina Nabizadeh, Justin Solomon
Infinite-dimensional orthonormal basis expansions play a central role in representing and computing with function spaces due to their favorable linear algebraic properties. However…
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…
Stochastic Wasserstein Barycenters
Sebastian Claici, Edward Chien, Justin Solomon
We present a stochastic algorithm to compute the barycenter of a set of probability distributions under the Wasserstein metric from optimal transport. Unlike previous approaches, o…