2 papers
cs.AI2026
Anchor Divergence for Semantic Geometry in Contrastive Learning
Akash Kannan, Kiho Park, Victor Veitch
This paper concerns how semantic context determines geometry in learned vector representations. Similarity is typically measured using cosine similarity, which provides a single fi…
cs.CV2023
Turn Down the Noise: Leveraging Diffusion Models for Test-time Adaptation via Pseudo-label Ensembling
Mrigank Raman, Rohan Shah, Akash Kannan +1
The goal of test-time adaptation is to adapt a source-pretrained model to a continuously changing target domain without relying on any source data. Typically, this is either done b…