68 citations · 114 across the 25 of their papers we have counts for
6 papers · 1 filter
Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors
Thomas Hartvigsen, Swami Sankaranarayanan, Hamid Palangi +2
Deployed language models decay over time due to shifting inputs, changing user needs, or emergent world-knowledge gaps. When such problems are identified, we want to make targeted…
Class-Specific Explainability for Deep Time Series Classifiers
Ramesh Doddaiah, Prathyush Parvatharaju, Elke Rundensteiner +1
Explainability helps users trust deep learning solutions for time series classification. However, existing explainability methods for multi-class time series classifiers focus on o…
Stop&Hop: Early Classification of Irregular Time Series
Thomas Hartvigsen, Walter Gerych, Jidapa Thadajarassiri +2
Early classification algorithms help users react faster to their machine learning model's predictions. Early warning systems in hospitals, for example, let clinicians improve their…
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations
Aparna Balagopalan, Haoran Zhang, Kimia Hamidieh +3
Machine learning models in safety-critical settings like healthcare are often blackboxes: they contain a large number of parameters which are not transparent to users. Post-hoc exp…
TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks
Ruofan Hu, Dongyu Zhang, Dandan Tao +3
Foodborne illness is a serious but preventable public health problem -- with delays in detecting the associated outbreaks resulting in productivity loss, expensive recalls, public…
ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection
Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi +3
Toxic language detection systems often falsely flag text that contains minority group mentions as toxic, as those groups are often the targets of online hate. Such over-reliance on…