1 citations · 1 across the 4 of their papers we have counts for
5 papers
Iterative Refinement Diffusion for Super-Resolved Data Assimilation of Multiscale Physical Systems
Mrigank Dhingra, Ramchandran Muthukumar, Rebecca Willett +1
Recovering high-resolution states from sparse, low-resolution observations is a central challenge in scientific machine learning and data assimilation. Classical data assimilation…
Diffusion models recover accurate mixture weights despite score function insensitivity
Andrew Dennehy, Ramchandran Muthukumar, Rebecca Willett +1
Score-based generative models exhibit a puzzling behavior: they often appear to cover all modes of a target multimodal distribution and yet may fail to learn the correct relative m…
Disentangling Safe and Unsafe Corruptions via Anisotropy and Locality
Ramchandran Muthukumar, Ambar Pal, Jeremias Sulam +1
State-of-the-art machine learning systems are vulnerable to small perturbations to their input, where ``small'' is defined according to a threat model that assigns a positive threa…
Sparsity-aware generalization theory for deep neural networks
Ramchandran Muthukumar, Jeremias Sulam
Deep artificial neural networks achieve surprising generalization abilities that remain poorly understood. In this paper, we present a new approach to analyzing generalization for…
Adversarial robustness of sparse local Lipschitz predictors
Ramchandran Muthukumar, Jeremias Sulam
This work studies the adversarial robustness of parametric functions composed of a linear predictor and a non-linear representation map. % that satisfies certain stability conditio…