Results 31 to 40 of about 36,894 (295)
Manifold for machine learning assurance [PDF]
Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: New Ideas and Emerging Results, 2020 The increasing use of machine-learning (ML) enabled systems in critical tasks fuels the quest for novel verification and validation techniques yet grounded in accepted system assurance principles. In traditional system development, model-based techniques have been widely adopted, where the central premise is that abstract models of the required system ...
Taejoon Byun, Sanjai Rayadurgam
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Learning a Manifold as an Atlas [PDF]
2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013 In this work, we return to the underlying mathematical definition of a manifold and directly characterise learning a manifold as finding an atlas, or a set of overlapping charts, that accurately describe local structure. We formulate the problem of learning the manifold as an optimisation that simultaneously refines the continuous parameters defining ...
Nikolaos Pitelis +2 more
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Manifold Learning via Manifold Deflation
CoRR, 2020 Nonlinear dimensionality reduction methods provide a valuable means to visualize and interpret high-dimensional data. However, many popular methods can fail dramatically, even on simple two-dimensional manifolds, due to problems such as vulnerability to noise, repeated eigendirections, holes in convex bodies, and boundary bias.
Daniel Ting, Michael I. Jordan
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Learning a manifold of fonts [PDF]
ACM Transactions on Graphics, 2014 The design and manipulation of typefaces and fonts is an area requiring substantial expertise; it can take many years of study to become a proficient typographer. At the same time, the use of typefaces is ubiquitous; there are many users who, while not experts, would like to be more involved in tweaking or changing existing fonts without suffering the ...
Neill D. F. Campbell, Jan Kautz
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Probabilistic learning on manifolds
Foundations of Data Science, 2020 41 pages, 4 ...
Soize, Christian, Ghanem, Roger
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Physical Review D, 2022 We propose a simple method to identify a continuous Lie algebra symmetry in a dataset through regression by an artificial neural network. Our proposal takes advantage of the $ \mathcal{O}(ε^2)$ scaling of the output variable under infinitesimal symmetry transformations on the input variables. As symmetry transformations are generated post-training, the
Sean Craven +3 more
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Hierarchical Manifold Learning [PDF]
, 2012 We present a novel method of hierarchical manifold learning which aims to automatically discover regional variations within images. This involves constructing manifolds in a hierarchy of image patches of increasing granularity, while ensuring consistency between hierarchy levels.
Kanwal K. Bhatia +5 more
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Remote Sensing, 2020 Synthetic Aperture Rradar (SAR) provides rich ground information for remote sensing survey and can be used all time and in all weather conditions. Polarimetric SAR (PolSAR) can further reveal surface scattering difference and improve radar’s ...
Chu He +3 more
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Rolling Element Bearing Fault Diagnosis Using Improved Manifold Learning
IEEE Access, 2017 Fault feature can be extracted by traditional manifold learning algorithms, which construct neighborhood graphs by Euclidean distance (ED). It is difficult to get an excellent dimensionality reduction result when processed data has strong correlations ...
Beibei Yao +3 more
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