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Relativistic triangle-curvature computing for federated HIV-1 protein-sequence monitoring. [PDF]

Sci Rep
Villalba-Díez J, González-Marcos A.
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Information-Theoretical Analysis of a Transformer-Based Generative AI Model. [PDF]

Entropy (Basel)
Deb M, Ogunfunmi T.
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Beyond Euclid: an illustrated guide to modern machine learning with geometric, topological, and algebraic structures. [PDF]

Mach Learn Sci Technol
Papillon M, Sanborn S, Mathe J, Cornelis L, Bertics A, Buracas D, J Lillemark H, Shewmake C, Dinc F, Pennec X, Miolane N.   +10 more
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Integral Betti signatures of brain, climate and financial networks compared to hyperbolic, Euclidean and spherical models. [PDF]

Sci Rep
Caputi L, Pidnebesna A, Hlinka J.
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The Riemannian Means Field Classifier for EEG-Based BCI Data. [PDF]

Sensors (Basel)
Andreev A, Cattan G, Congedo M.
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Isolated steady solutions of the 3D Euler equations. [PDF]

Proc Natl Acad Sci U S A
Enciso A, Kepplinger W, Peralta-Salas D.
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A Sharp Quantitative Alexandrov Inequality and Applications to Volume Preserving Geometric Flows in 3D. [PDF]

Arch Ration Mech Anal
Julin V, Morini M, Oronzio F, Spadaro E.
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Riemannian Manifold Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
Recently, manifold learning has been widely exploited in pattern recognition, data analysis, and machine learning. This paper presents a novel framework, called Riemannian manifold learning (RML), based on the assumption that the input high-dimensional data lie on an intrinsically low-dimensional Riemannian manifold.
Hongbin Zha
exaly   +3 more sources