Zobrazeno 1 - 10
of 917
pro vyhledávání: '"Riemannian approach"'
Optimal transport (OT) theory has attracted much attention in machine learning and signal processing applications. OT defines a notion of distance between probability distributions of source and target data points. A crucial factor that influences OT
Externí odkaz:
http://arxiv.org/abs/2409.10085
Tensor networks offer a valuable framework for implementing Lindbladian dynamics in many-body open quantum systems with nearest-neighbor couplings. In particular, a tensor network ansatz known as the Locally Purified Density Operator employs the loca
Externí odkaz:
http://arxiv.org/abs/2409.08127
Autor:
Khanam, Tahmina, Laga, Hamid, Bennamoun, Mohammed, Wang, Guanjin, Sohel, Ferdous, Boussaid, Farid, Wang, Guan, Srivastava, Anuj
We propose the first comprehensive approach for modeling and analyzing the spatiotemporal shape variability in tree-like 4D objects, i.e., 3D objects whose shapes bend, stretch, and change in their branching structure over time as they deform, grow,
Externí odkaz:
http://arxiv.org/abs/2408.12443
Through the use of sub-Riemannian metrics we provide quantitative estimates for the maximal tight neighbourhood of a Reeb orbit on a three-dimensional contact manifold. Under appropriate geometric conditions we show how to construct closed curves whi
Externí odkaz:
http://arxiv.org/abs/2407.00770
We propose a novel Riemannian method for solving the Extreme multi-label classification problem that exploits the geometric structure of the sparse low-dimensional local embedding models. A constrained optimization problem is formulated as an optimiz
Externí odkaz:
http://arxiv.org/abs/2109.15021
Autor:
Riquelme, Alvaro
In geosciences, the use of classical Euclidean methods is unsuitable for treating and analyzing some types of data, as this may not belong to a vector space. This is the case for correlation matrices, belonging to a subfamily of symmetric positive de
Externí odkaz:
http://arxiv.org/abs/2109.14550
Learning faithful graph representations as sets of vertex embeddings has become a fundamental intermediary step in a wide range of machine learning applications. We propose the systematic use of symmetric spaces in representation learning, a class en
Externí odkaz:
http://arxiv.org/abs/2106.04941
Autor:
Benkarim, Oualid, Paquola, Casey, Park, Bo-yong, Royer, Jessica, Rodríguez-Cruces, Raúl, Vos de Wael, Reinder, Misic, Bratislav, Piella, Gemma, Bernhardt, Boris C.
Publikováno v:
In NeuroImage 15 August 2022 257
Kniha
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
In Image and Vision Computing March 2020 95