6 papers
Bottlenecked semantic compression by representation reconstruction
Xiaoya Tang, Shilong Liu, Daniel Smolyak +3
Vision Transformers incur high computational costs when processing long visual token sequences. Existing reduction methods typically rank tokens independently and discard those dee…
From "I have nothing to hide" to "It looks like stalking": Measuring Americans' Level of Comfort with Individual Mobility Features Extracted from Location Data
Naman Awasthi, Saad Mohammad Abrar, Daniel Smolyak +1
Location data collection has become widespread with smart phones becoming ubiquitous. Smart phone apps often collect precise location data from users by offering \textit{free} serv…
Systematic analysis of the effectiveness of adding human mobility data to covid-19 case prediction linear models
Saad Mohammad Abrar, Naman Awasthi, Daniel Smolyak +1
Human mobility data has been extensively used in covid-19 case prediction models. Nevertheless, related work has questioned whether mobility data really helps that much. We present…
DemOpts: Fairness corrections in COVID-19 case prediction models
Naman Awasthi, Saad Abrar, Daniel Smolyak +1
COVID-19 forecasting models have been used to inform decision making around resource allocation and intervention decisions e.g., hospital beds or stay-at-home orders. State of the…
Assessing the Impact of Case Correction Methods on the Fairness of COVID-19 Predictive Models
Daniel Smolyak, Saad Abrar, Naman Awasthi +1
One of the central difficulties of addressing the COVID-19 pandemic has been accurately measuring and predicting the spread of infections. In particular, official COVID-19 case cou…
Auditing the Fairness of the US COVID-19 Forecast Hub's Case Prediction Models
Saad Mohammad Abrar, Naman Awasthi, Daniel Smolyak +1
The US COVID-19 Forecast Hub, a repository of COVID-19 forecasts from over 50 independent research groups, is used by the Centers for Disease Control and Prevention (CDC) for their…