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Found 4 papers, 4 papers with code
Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome
Our work extends the success of data augmentation to compositional data, i. e., simplex-valued data, which is of particular interest in the context of the human microbiome.
On the Normalizing Constant of the Continuous Categorical Distribution
This family enjoys remarkable mathematical simplicity; its density function resembles that of the Dirichlet distribution, but with a normalizing constant that can be written in closed form using elementary functions only.
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning
Modern deep learning is primarily an experimental science, in which empirical advances occasionally come at the expense of probabilistic rigor.
The continuous categorical: a novel simplex-valued exponential family
Simplex-valued data appear throughout statistics and machine learning, for example in the context of transfer learning and compression of deep networks.
