LOCA is a deep learning-based method for obtaining standardized data coordinates from scientific measurements. Data observations are modeled as samples from an unknown, nonlinear deformation of an underlying Riemannian manifold, which is parametrized by a few normalized, latent variables.
Apr 15, 2020 · We propose a deep-learning based method for obtaining standardized data coordinates from scientific measurements.
Dec 8, 2020 · LOCA is a deep learning-based method for obtaining standardized data coordinates from scientific measurements.
Apr 20, 2020 · We propose a deep-learning based method for obtaining standardized data coordinates from scientific measurements.
A method for extracting standardized, nonlinear, intrinsic coordinates from measured data, leading to a generalized isometric embedding of the observations, ...
We develop a method for extracting standardized, nonlinear, intrinsic coordinates from measured data, leading to a generalized isometric embedding of the ...
Duration: 31:30
Posted: Sep 7, 2020
Posted: Sep 7, 2020
Missing: LOCA: standardized data coordinates.
This is achieved through a local burst data acquisition strategy that allows us to capture the local z-scored structure. We implement this method using a local ...
Missing: LOCA: | Show results with:LOCA:
[PDF] Isometric Embedding of Manifolds with Auto-Encoder Networks - mediaTUM
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Sep 22, 2022 · We use Auto-Encoders in the context of Manifold Learning to learn lower dimensional embeddings of higher dimensional manifolds.
We propose a deep-learning based method for obtaining standardized data coordinates from scientific measurements. Data observations are modeled as samples from ...