9 citations · 13 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 9 cited
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…
stat.ML2023★ 4 cited
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…