1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.CV2025
Attention IoU: Examining Biases in CelebA using Attention Maps
Aaron Serianni, Tyler Zhu, Olga Russakovsky +1
Computer vision models have been shown to exhibit and amplify biases across a wide array of datasets and tasks. Existing methods for quantifying bias in classification models prima…
cs.LG2023★ 1 cited
Training-free Neural Architecture Search for RNNs and Transformers
Aaron Serianni, Jugal Kalita
Neural architecture search (NAS) has allowed for the automatic creation of new and effective neural network architectures, offering an alternative to the laborious process of manua…