比利时vs摩洛哥足彩
,
university of california san diego
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math 278b - mathematics of information, data, and signals seminar
yariv aizenbud
yale university
non-parametric estimation of manifolds from noisy data
abstract:
a common task in many data-driven applications is to find a low dimensional manifold that describes the data accurately. estimating a manifold from noisy samples has proven to be a challenging task. indeed, even after decades of research, there is no (computationally tractable) algorithm that accurately estimates a manifold from noisy samples with a constant level of noise. in this talk, we will present a method that estimates a manifold and its tangent in the ambient space. moreover, we establish rigorous convergence rates, which are essentially as good as existing convergence rates for function estimation. this is a joint work with barak sober.
host: alex cloninger
september 30, 2021
11:30 am
virtual talk zoom link: https://ucsd.zoom.us/j/98762502667
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