Zobrazeno 1 - 10
of 358
pro vyhledávání: '"Hauenstein, Jonathan"'
Autor:
Kahle, David, Hauenstein, Jonathan D
Nonlinear systems of polynomial equations arise naturally in many applied settings, for example loglinear models on contingency tables and Gaussian graphical models. The solution sets to these systems over the reals are often positive dimensional spa
Externí odkaz:
http://arxiv.org/abs/2410.16071
Autor:
Abraham, Sophia J., Huang, Jin, RichardWebster, Brandon, Milford, Michael, Hauenstein, Jonathan D., Scheirer, Walter
We introduce a unique semantic segmentation dataset of 6,096 high-resolution aerial images capturing indigenous and invasive grass species in Bega Valley, New South Wales, Australia, designed to address the underrepresented domain of ecological data
Externí odkaz:
http://arxiv.org/abs/2408.06356
A standard question in real algebraic geometry is to compute the number of connected components of a real algebraic variety in affine space. By adapting an approach for determining connectivity in complements of real hypersurfaces by Hong, Rohal, Saf
Externí odkaz:
http://arxiv.org/abs/2405.18578
The field of numerical algebraic geometry consists of algorithms for numerically solving systems of polynomial equations. When the system is exact, such as having rational coefficients, the solution set is well-defined. However, for a member of a par
Externí odkaz:
http://arxiv.org/abs/2403.18749
There are many numerical methods for solving partial different equations (PDEs) on manifolds such as classical implicit, finite difference, finite element, and isogeometric analysis methods which aim at improving the interoperability between finite e
Externí odkaz:
http://arxiv.org/abs/2311.09866
Autor:
Cummings, Joseph, Hauenstein, Jonathan
We describe an algorithm for computing Macaulay dual spaces for multi-graded ideals. For homogeneous ideals, the natural grading is inherited by the Macaulay dual space which has been leveraged to develop algorithms to compute the Macaulay dual space
Externí odkaz:
http://arxiv.org/abs/2310.11587
Autor:
Abraham, Sophia J., Maduranga, Kehelwala D. G., Kinnison, Jeffery, Carmichael, Zachariah, Hauenstein, Jonathan D., Scheirer, Walter J.
Machine learning has achieved remarkable success over the past couple of decades, often attributed to a combination of algorithmic innovations and the availability of high-quality data available at scale. However, a third critical component is the fi
Externí odkaz:
http://arxiv.org/abs/2308.03317
Autor:
Wang, Yu, Cobian, Emma R., Lee, Jubilee, Liu, Fang, Hauenstein, Jonathan D., Schiavazzi, Daniele E.
Variational inference is an increasingly popular method in statistics and machine learning for approximating probability distributions. We developed LINFA (Library for Inference with Normalizing Flow and Annealing), a Python library for variational i
Externí odkaz:
http://arxiv.org/abs/2307.04675
The maximum function, on vectors of real numbers, is not differentiable. Consequently, several differentiable approximations of this function are popular substitutes. We survey three smooth functions which approximate the maximum function and analyze
Externí odkaz:
http://arxiv.org/abs/2306.11506
Autor:
Bates, Daniel J., Breiding, Paul, Chen, Tianran, Hauenstein, Jonathan D., Leykin, Anton, Sottile, Frank
Numerical nonlinear algebra is a computational paradigm that uses numerical analysis to study polynomial equations. Its origins were methods to solve systems of polynomial equations based on the classical theorem of B\'ezout. This was decisively link
Externí odkaz:
http://arxiv.org/abs/2302.08585