1 citations · 2 across the 4 of their papers we have counts for
4 papers
Assessing Generative Models for Structured Data
Reilly Cannon, Nicolette M. Laird, Caesar Vazquez +3
Synthetic tabular data generation has emerged as a promising method to address limited data availability and privacy concerns. With the sharp increase in the performance of large l…
Understanding Generative AI Content with Embedding Models
Max Vargas, Reilly Cannon, Andrew Engel +2
Constructing high-quality features is critical to any quantitative data analysis. While feature engineering was historically addressed by carefully hand-crafting data representatio…
Measuring training variability from stochastic optimization using robust nonparametric testing
Sinjini Banerjee, Tim Marrinan, Reilly Cannon +2
Deep neural network training often involves stochastic optimization, meaning each run will produce a different model. This implies that hyperparameters of the training process, suc…
Robust Nonparametric Hypothesis Testing to Understand Variability in Training Neural Networks
Sinjini Banerjee, Reilly Cannon, Tim Marrinan +2
Training a deep neural network (DNN) often involves stochastic optimization, which means each run will produce a different model. Several works suggest this variability is negligib…