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cs.CV2019
Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian Reparameterization offers Significant Performance and Efficiency Gains
Sathya N. Ravi, Abhay Venkatesh, Glenn Moo Fung +1
Data dependent regularization is known to benefit a wide variety of problems in machine learning. Often, these regularizers cannot be easily decomposed into a sum over a finite num…
math.AT2019
Approximating the Convex Hull via Metric Space Magnitude
Glenn Fung, Eric Bunch, Dan Dickinson
Magnitude of a finite metric space and the related notion of magnitude functions on metric spaces is an active area of research in algebraic topology. Magnitude originally arose in…