26 citations · 28 across the 8 of their papers we have counts for
3 papers · 1 filter
Improved Imaging by Invex Regularizers with Global Optima Guarantees
Samuel Pinilla, Tingting Mu, Neil Bourne +1
Image reconstruction enhanced by regularizers, e.g., to enforce sparsity, low rank or smoothness priors on images, has many successful applications in vision tasks such as computer…
Data-driven Approaches to Surrogate Machine Learning Model Development
H. Rhys Jones, Tingting Mu, Andrei C. Popescu +1
We demonstrate the adaption of three established methods to the field of surrogate machine learning model development. These methods are data augmentation, custom loss functions an…
Bias-Variance Decompositions for Margin Losses
Danny Wood, Tingting Mu, Gavin Brown
We introduce a novel bias-variance decomposition for a range of strictly convex margin losses, including the logistic loss (minimized by the classic LogitBoost algorithm), as well…