5 citations · 5 across the 3 of their papers we have counts for
3 papers
Come Together: Analyzing Popular Songs Through Statistical Embeddings
Matthew Esmaili Mallory, Mark Glickman, Jason Brown
Statistical modeling of popular music presents a unique challenge due to the complexity of song structures, which cannot be easily analyzed using conventional statistical tools. Ho…
Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation
Matthew Esmaili Mallory, Kevin Han Huang, Morgane Austern
Over the last decade, a wave of research has characterized the exact asymptotic risk of many high-dimensional models in the proportional regime. Two foundational results have drive…
Arbitrary-Depth Universal Approximation Theorems for Operator Neural Networks
Annan Yu, Chloé Becquey, Diana Halikias +2
The standard Universal Approximation Theorem for operator neural networks (NNs) holds for arbitrary width and bounded depth. Here, we prove that operator NNs of bounded width and a…