3 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 2 cited
Target Domain Data induces Negative Transfer in Mixed Domain Training with Disjoint Classes
Eryk Banatt, Vickram Rajendran, Liam Packer
In practical scenarios, it is often the case that the available training data within the target domain only exist for a limited number of classes, with the remaining classes only a…
cs.CV2021
Shape-Biased Domain Generalization via Shock Graph Embeddings
Maruthi Narayanan, Vickram Rajendran, Benjamin Kimia
There is an emerging sense that the vulnerability of Image Convolutional Neural Networks (CNN), i.e., sensitivity to image corruptions, perturbations, and adversarial attacks, is c…
cs.LG2019★ 3 cited
Accurate Layerwise Interpretable Competence Estimation
Vickram Rajendran, William LeVine
Estimating machine learning performance 'in the wild' is both an important and unsolved problem. In this paper, we seek to examine, understand, and predict the pointwise competence…