How Different are Sales Tax Rates Along Georgia's Border? - Brief

This brief provides a comparison of sales tax rates in counties on Georgia's borders. FRC Brief 9

Teen Childbearing and Public Assistance in Georgia - Brief

This brief examines the link between teen births and welfare. FRC Brief 10

The Link Between Teen Childbearing and Employment in Georgia - Brief

This brief analyzes teen births and employment of teen mothers. (May 2005) FRC Brief 10

An Analysis of a Need-Based Student Aid Program for Georgia - Brief

This report explores issues associated with establishing a need-based student aid program in Georgia. FRC Brief 17

An Analysis of a Need-Based Student Aid Program for Georgia

This report explores issues associated with establishing a need-based student aid program in Georgia. FRC Report 17

Limitations on Increases in Property Tax Assessed Value

This report describes how various states limit the growth in property tax assessment and explores the implications of such limitations

The Fair Tax and Its Effect on Georgia - Brief

This brief analyzes the impacts of a national retail sales tax on Georgians. FRC Brief 11

Status of Women in Atlanta:A Survey of Economic Demographic, and Social Indicators for the 15-County Area

This report provides a detailed overview of economic, demographic and social aspects of women and girls in the metro Atlanta region. FRC Report 15

On the Interpretability of Attention Networks

Attention mechanisms form a core component of several successful deep learning architectures, and are based on one key idea: ''The output depends only on a small (but unknown) segment of the input.'' In several practical applications like image captioning and language translation, this is mostly true. In trained models with an attention mechanism, the outputs of an intermediate module that encodes the segment of input responsible for the output is often used as a way to peek into the `reasoning` of the network. We make such a notion more precise for a variant of the classification problem that we term selective dependence classification (SDC) when used with attention model architectures. Under such a setting, we demonstrate various error modes where an attention model can be accurate but fail to be interpretable, and show that such models do occur as a result of training. We illustrate various situations that can accentuate and mitigate this behaviour. Finally, we use our objective definition of interpretability for SDC tasks to evaluate a few attention model learning algorithms designed to encourage sparsity and demonstrate that these algorithms help improve interpretability.Comment: ACML 2022, proceedings to be appeared in PMLR, Volume 18

Job Creation by Georgia Start-up Businesses

Abstract not available. Report #7