11 citations · 17 across the 16 of their papers we have counts for
10 papers · 1 filter
Adaptive Volumetric Parameterization of Simply Connected 3-Manifolds with Applications
Zhiyuan Lyu, Qiguang Chen, Lok Ming Lui +1
Volumetric parameterization, the process of mapping a 3-manifold onto a simplified volumetric domain, is important for many tasks in computer graphics and imaging science. However,…
Exact Schur-Sylvester Dimensionality Reductions for Non-Smooth Stochastic Complexity and Manifold Sampling
Trenton Lau, Gary P. T. Choi
The exact computation of the Normalized Maximum Likelihood (NML) codelength for regular non-smooth estimators (e.g., Lasso) has been historically limited by the cubic scaling walls…
Learning the Geometry of Data: A Mathematical Review of Shape Space Analysis
Gary P. T. Choi, Khanh Dao Duc, Shira Faigenbaum-Golovin +6
A central objective of machine learning is to identify structure and patterns in data. Advances in data acquisition have increasingly produced datasets whose observations possess r…
How geometry of subduction zones correlates with earthquake dynamics
Oscar Y. L. Chau, Rebecca Bendick, Gary P. T. Choi +1
Subduction zones on the surface of the Earth, where abrupt sliding leads to earthquakes, are generally curved and localized. How does the geometry of these zones influence the occu…
Resolution-free neural surrogates for geometric parameterization and mapping with spatially varying fields
Yanwen Huang, Lok Ming Lui, Gary P. T. Choi
Many imaging problems require computing spatial transformations induced by spatially varying intensity, feature, or density fields. Canonical examples include distortion correction…
The Normalized Maximum Likelihood for Regular Non-Smooth Models: Measure-Theoretic Foundations and Geometric Sampling
Trenton Lau, Gary P. T. Choi
The Normalized Maximum Likelihood (NML) codelength, or stochastic complexity, represents a principled criterion for universal coding. While recent coarea-based formulations provide…