20 citations · 38 across the 8 of their papers we have counts for
3 papers · 1 filter
InfoNCE Loss Provably Learns Cluster-Preserving Representations
Advait Parulekar, Liam Collins, Karthikeyan Shanmugam +2
The goal of contrasting learning is to learn a representation that preserves underlying clusters by keeping samples with similar content, e.g. the ``dogness'' of a dog, close to ea…
Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD
Matthew Faw, Litu Rout, Constantine Caramanis +1
This work considers the problem of finding a first-order stationary point of a non-convex function with potentially unbounded smoothness constant using a stochastic gradient oracle…
A Theoretical Justification for Image Inpainting using Denoising Diffusion Probabilistic Models
Litu Rout, Advait Parulekar, Constantine Caramanis +1
We provide a theoretical justification for sample recovery using diffusion based image inpainting in a linear model setting. While most inpainting algorithms require retraining wit…