Cover Image

Differential geometry with extreme eigenvalues in the positive semidefinite cone

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mostajeran, Cyrus, Da Costa, Nathaël, Van Goffrier, Graham, Sepulchre, Rodolphe
Format:	Preprint
Published:	2023
Subjects:	Differential Geometry Computation Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Geometric statistics with subspace structure preservation for SPD matrices
by: Mostajeran, Cyrus, et al.
Published: (2024)

Geometric Learning with Positively Decomposable Kernels
by: Da Costa, Nathael, et al.
Published: (2023)

Universal kernels via harmonic analysis on Riemannian symmetric spaces
by: Steinert, Franziskus, et al.
Published: (2025)

Invariant kernels on Riemannian symmetric spaces: a harmonic-analytic approach
by: Da Costa, Nathael, et al.
Published: (2023)

Geometric Gaussian Approximations of Probability Distributions
by: Da Costa, Nathaël, et al.
Published: (2025)

Programming evolution of geometry in shape-morphing sheets via spatiotemporal activation
by: Duffy, Daniel, et al.
Published: (2024)

Gradient modelling of memristive systems
by: Forni, Fulvio, et al.
Published: (2025)

Jet Functors and Weil Algebras in Automatic Differentiation: A Geometric Analysis
by: Sangha, Amandip
Published: (2025)

Canonical normalizing flows for manifold learning
by: Flouris, Kyriakos, et al.
Published: (2023)

Geometric eigenvalue estimates of Kuttler-Sigillito type on differential forms
by: Assali, Rodolphe Abou
Published: (2026)

Variational autoencoders with latent high-dimensional steady geometric flows for dynamics
by: Gracyk, Andrew
Published: (2024)

A singular Riemannian Geometry Approach to Deep Neural Networks III. Piecewise Differentiable Layers and Random Walks on $n$-dimensional Classes
by: Benfenati, Alessandro, et al.
Published: (2024)

Emergent Riemannian geometry over learning discrete computations on continuous manifolds
by: Brandon, Julian, et al.
Published: (2025)

Garding cones and positivity of curvature operators
by: Huang, Teng, et al.
Published: (2025)

Wishart cones and quantum geometry
by: Combe, Noemie C.
Published: (2024)

The Curved Spacetime of Transformer Architectures
by: Di Sipio, Riccardo, et al.
Published: (2025)

Cartan geometries with model the future lightlike cone of Lorentz-Minkowski spacetime
by: Morón, Rodrigo, et al.
Published: (2025)

Space filling positionality and the Spiroformer
by: Maurin, M., et al.
Published: (2025)

The rigidity of eigenvalues on Kähler manifolds with positive Ricci lower bound
by: Chu, Jianchun, et al.
Published: (2024)

Tangent groupoid and tangent cones in sub-Riemannian geometry
by: Mohsen, Omar
Published: (2022)

Surrogate-Based Differentiable Pipeline for Shape Optimization
by: Rehmann, Andrin, et al.
Published: (2025)

Low-dimensional approximations of the conditional law of Volterra processes: a non-positive curvature approach
by: Arabpour, Reza, et al.
Published: (2024)

Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution
by: Zaher, Eslam, et al.
Published: (2024)

Stability of extremal domains for the first eigenvalue of the Laplacian operator
by: Cavalcante, Marcos P., et al.
Published: (2024)

Barycentric subspace analysis of network-valued data
by: Maignant, Elodie, et al.
Published: (2025)

Learning Beyond Euclid: Curvature-Adaptive Generalization for Neural Networks on Manifolds
by: Sarkar, Krisanu
Published: (2025)

Hardness of Learning Neural Networks under the Manifold Hypothesis
by: Kiani, Bobak T., et al.
Published: (2024)

Cartan moving frames and the data manifolds
by: Tron, Eliot, et al.
Published: (2024)

Universal Collection of Euclidean Invariants between Pairs of Position-Orientations
by: Bellaard, Gijs, et al.
Published: (2025)

Roto-Translation Invariant Metrics on Position-Orientation Space
by: Bellaard, Gijs, et al.
Published: (2025)

Flow Matching on Lie Groups
by: Sherry, Finn M., et al.
Published: (2025)

Kernel-Gradient Drifting Models
by: Esteban-Casadevall, Maria, et al.
Published: (2026)

Robust Tangent Space Estimation via Laplacian Eigenvector Gradient Orthogonalization
by: Kohli, Dhruv, et al.
Published: (2025)

Discretized Gradient Flow for Manifold Learning in the Space of Embeddings
by: Gold, Dara, et al.
Published: (2019)

Minimising Willmore Energy via Neural Flow
by: Hirst, Edward, et al.
Published: (2026)

Interpretable Analytic Calabi-Yau Metrics via Symbolic Distillation
by: Eng, D Yang
Published: (2026)

Steerable Neural ODEs on Homogeneous Spaces
by: Andersdotter, Emma, et al.
Published: (2026)

Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds
by: Sun, Xingzhi, et al.
Published: (2024)

Deep Generative Models: Complexity, Dimensionality, and Approximation
by: Wang, Kevin, et al.
Published: (2025)

Riemannian Integrated Gradients: A Geometric View of Explainable AI
by: Costanza, Federico, et al.
Published: (2025)