2 citations · 2 across the 3 of their papers we have counts for
4 papers · 1 filter
DECIDER: Leveraging Foundation Model Priors for Improved Model Failure Detection and Explanation
Rakshith Subramanyam, Kowshik Thopalli, Vivek Narayanaswamy +1
Reliably detecting when a deployed machine learning model is likely to fail on a given input is crucial for ensuring safe operation. In this work, we propose DECIDER (Debiasing Cla…
Speeding Up Image Classifiers with Little Companions
Yang Liu, Kowshik Thopalli, Jayaraman Thiagarajan
Scaling up neural networks has been a key recipe to the success of large language and vision models. However, in practice, up-scaled models can be disproportionately costly in term…
Target-Aware Generative Augmentations for Single-Shot Adaptation
Kowshik Thopalli, Rakshith Subramanyam, Pavan Turaga +1
In this paper, we address the problem of adapting models from a source domain to a target domain, a task that has become increasingly important due to the brittle generalization of…
Domain Alignment Meets Fully Test-Time Adaptation
Kowshik Thopalli, Pavan Turaga, Jayaraman J. Thiagarajan
A foundational requirement of a deployed ML model is to generalize to data drawn from a testing distribution that is different from training. A popular solution to this problem is…