Zobrazeno 1 - 10
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pro vyhledávání: '"Stalzer, Mark A."'
Autor:
Ju, Chao, Stalzer, Mark
Because only two variables are needed to characterize a simple thermodynamic system in equilibrium, any such system is constrained on a 2D manifold. Of particular interest are the exact 1-forms on the cotangent space of that manifold, since the integ
Externí odkaz:
http://arxiv.org/abs/1808.03863
Autor:
Stewart, Lawrence, Stalzer, Mark
When applying Machine Learning techniques to problems, one must select model parameters to ensure that the system converges but also does not become stuck at the objective function's local minimum. Tuning these parameters becomes a non-trivial task f
Externí odkaz:
http://arxiv.org/abs/1709.07842
Autor:
Stalzer, Mark A., Ju, Chao
There is sufficient information in the far-field of a radiating dipole antenna to rediscover the Maxwell Equations and the wave equations of light, including the speed of light $c.$ TheoSea is a Julia program that does this in about a second, and the
Externí odkaz:
http://arxiv.org/abs/1708.04927
Autor:
Stalzer, Mark A.
Presented is a Julia meta-program that discovers compact theories from data if they exist. It writes candidate theories in Julia and then validates: tossing the bad theories and keeping the good theories. Compactness is measured by a metric: such as
Externí odkaz:
http://arxiv.org/abs/1706.06975
Autor:
Xu, Wenqing, Stalzer, Mark
In various subjects, there exist compact and consistent relationships between input and output parameters. Discovering the relationships, or namely compact laws, in a data set is of great interest in many fields, such as physics, chemistry, and finan
Externí odkaz:
http://arxiv.org/abs/1706.05123
Autor:
Stalzer, Mark, Mentzel, Chris
The Gordon and Betty Moore Foundation ran an Investigator Competition as part of its Data-Driven Discovery Initiative in 2014. We received about 1,100 applications and each applicant had the opportunity to list up to five influential works in the gen
Externí odkaz:
http://arxiv.org/abs/1503.08776
Publikováno v:
Parallel Processing Letters 24, 1450004 (2014)
We analyze the parallel performance of randomized interpolative decomposition by decomposing low rank complex-valued Gaussian random matrices up to 64 GB. We chose a Cray XMT supercomputer as it provides an almost ideal PRAM model permitting quick in
Externí odkaz:
http://arxiv.org/abs/1205.3830
Akademický článek
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Autor:
Stalzer, Mark1 stalzer@caltech.edu, Mentzel, Chris1 chris.mentzel@moore.org
Publikováno v:
SpringerPlus. 8/5/2016, Vol. 5 Issue 1, p1-17. 17p.
Akademický článek
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