Results 271 to 280 of about 59,568 (301)

Manifold lateralisation and variability in the language connectome at 7T

Dulyan L, Bortolami C, Chacón EG, Beyh A, de Schotten MT, Forkel S.   +5 more
europepmc   +1 more source

Theoretical Framework, Technical Evolution, and Future Prospects of Cross-Modal Mapping and Controllable Image Generation Under Multi-Source Heterogeneous Collaboration. [PDF]

Sensors (Basel)
Chen M, Lin Z, Song X, Luo Y, Duan X, Li S, Xie C.   +6 more
europepmc   +1 more source

Quantum circuit complexity and unsupervised machine learning of topological order. [PDF]

Nat Commun
Che Y, Gneiting C, Wang X, Nori F.
europepmc   +1 more source

Exponential statistical manifold

Annals of the Institute of Statistical Mathematics, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
CENA A, PISTONE, Giovanni
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Diffusion on Statistical Manifolds

2006 International Conference on Image Processing, 2006
This paper presents a new diffusion scheme on statistical manifolds for the detection of texture boundaries. The technique derives from our previous work, in which 2-dimensional Riemannian manifolds were statistically defined by maps that transform a parameter domain onto a set of probability density functions.
Sang-Mook Lee, A. Lynn Abbott, Neil A. Clark, Philip A. Araman   +3 more
openaire   +1 more source

Musical Isomorphisms and Statistical Manifolds

Mediterranean Journal of Mathematics, 2022
On a pseudo-Riemannian manifold \((M, g)\), an affine connection \(\nabla\) is called a Codazzi connection if \(\nabla g\) is totally symmetric. The triple \((M,g,\nabla)\) is called a statistical manifold if \(\nabla\) is torsion free and Codazzi. This is the setting of information geometry.
Esmaeil Peyghan, Leila Nourmohammadifar, Siraj Uddin   +2 more
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Diffusion Kernels on Statistical Manifolds

J. Mach. Learn. Res., 2005
A family of kernels for statistical learning is introduced that exploits the geometric structure of statistical models. The kernels are based on the heat equation on the Riemannian manifold defined by the Fisher information metric associated with a statistical family, and generalize the Gaussian kernel of Euclidean space.
John D. Lafferty, Guy Lebanon
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Information geometry and statistical manifold

Chaos, Solitons & Fractals, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Abdel-All, Nassar H., Abd-Ellah, H. N., Moustafa, H. M.   +2 more
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