649 citations · 914 across the 10 of their papers we have counts for
10 papers
Bias Mitigation Framework for Intersectional Subgroups in Neural Networks
Narine Kokhlikyan, Bilal Alsallakh, Fulton Wang +3
We propose a fairness-aware learning framework that mitigates intersectional subgroup bias associated with protected attributes. Prior research has primarily focused on mitigating…
Prescriptive and Descriptive Approaches to Machine-Learning Transparency
David Adkins, Bilal Alsallakh, Adeel Cheema +7
Specialized documentation techniques have been developed to communicate key facts about machine-learning (ML) systems and the datasets and models they rely on. Techniques such as D…
A Tour of Visualization Techniques for Computer Vision Datasets
Bilal Alsallakh, Pamela Bhattacharya, Vanessa Feng +4
We survey a number of data visualization techniques for analyzing Computer Vision (CV) datasets. These techniques help us understand properties and latent patterns in such data, by…
Investigating sanity checks for saliency maps with image and text classification
Narine Kokhlikyan, Vivek Miglani, Bilal Alsallakh +2
Saliency maps have shown to be both useful and misleading for explaining model predictions especially in the context of images. In this paper, we perform sanity checks for text mod…
Investigating Saturation Effects in Integrated Gradients
Vivek Miglani, Narine Kokhlikyan, Bilal Alsallakh +2
Integrated Gradients has become a popular method for post-hoc model interpretability. De-spite its popularity, the composition and relative impact of different regions of the integ…
Mind the Pad -- CNNs can Develop Blind Spots
Bilal Alsallakh, Narine Kokhlikyan, Vivek Miglani +2
We show how feature maps in convolutional networks are susceptible to spatial bias. Due to a combination of architectural choices, the activation at certain locations is systematic…
Captum: A unified and generic model interpretability library for PyTorch
Narine Kokhlikyan, Vivek Miglani, Miguel Martin +8
In this paper we introduce a novel, unified, open-source model interpretability library for PyTorch [12]. The library contains generic implementations of a number of gradient and p…
Visualizing Classification Structure of Large-Scale Classifiers
Bilal Alsallakh, Zhixin Yan, Shabnam Ghaffarzadegan +2
We propose a measure to compute class similarity in large-scale classification based on prediction scores. Such measure has not been formally pro-posed in the literature. We show h…
Prediction Scores as a Window into Classifier Behavior
Medha Katehara, Emma Beauxis-Aussalet, Bilal Alsallakh
Most multi-class classifiers make their prediction for a test sample by scoring the classes and selecting the one with the highest score. Analyzing these prediction scores is usefu…
Do Convolutional Neural Networks Learn Class Hierarchy?
Bilal Alsallakh, Amin Jourabloo, Mao Ye +2
Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possib…