Image de couverture de livre

The Curved Spacetime of Transformer Architectures

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Di Sipio, Riccardo, Diaz-Rodriguez, Jairo, Serrano, Luis
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Computation and Language Differential Geometry
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Revisiting Anisotropy in Language Transformers: The Geometry of Learning Dynamics
par: Bernas, Raphael, et autres
Publié: (2026)

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

Canonical normalizing flows for manifold learning
par: Flouris, Kyriakos, et autres
Publié: (2023)

Differential geometry with extreme eigenvalues in the positive semidefinite cone
par: Mostajeran, Cyrus, et autres
Publié: (2023)

Conformal Transformation of Kernels: A Geometric Perspective on Text Classification
par: Rădulescu, Ioana, et autres
Publié: (2024)

Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution
par: Zaher, Eslam, et autres
Publié: (2024)

Geometric statistics with subspace structure preservation for SPD matrices
par: Mostajeran, Cyrus, et autres
Publié: (2024)

On Computing Elastic Shape Distances between Curves in d-dimensional Space
par: Bernal, Javier, et autres
Publié: (2024)

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

Barycentric subspace analysis of network-valued data
par: Maignant, Elodie, et autres
Publié: (2025)

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

Universal Collection of Euclidean Invariants between Pairs of Position-Orientations
par: Bellaard, Gijs, et autres
Publié: (2025)

Roto-Translation Invariant Metrics on Position-Orientation Space
par: Bellaard, Gijs, et autres
Publié: (2025)

Flow Matching on Lie Groups
par: Sherry, Finn M., et autres
Publié: (2025)

Robust Tangent Space Estimation via Laplacian Eigenvector Gradient Orthogonalization
par: Kohli, Dhruv, et autres
Publié: (2025)

Deep Generative Models: Complexity, Dimensionality, and Approximation
par: Wang, Kevin, et autres
Publié: (2025)

Riemannian Integrated Gradients: A Geometric View of Explainable AI
par: Costanza, Federico, et autres
Publié: (2025)

Distance Measure Based on an Embedding of the Manifold of K-Component Gaussian Mixture Models into the Manifold of Symmetric Positive Definite Matrices
par: Vishwakarma, Amit, et autres
Publié: (2025)

Fiber Bundle Networks: A Geometric Machine Learning Paradigm
par: Liu, Dong
Publié: (2025)

Universal kernels via harmonic analysis on Riemannian symmetric spaces
par: Steinert, Franziskus, et autres
Publié: (2025)

Tangentially Aligned Integrated Gradients for User-Friendly Explanations
par: Simpson, Lachlan, et autres
Publié: (2025)

Simultaneous Optimization of Geodesics and Fréchet Means
par: Rygaard, Frederik Möbius, et autres
Publié: (2025)

Staying on the Manifold: Geometry-Aware Noise Injection
par: Jacobsen, Albert Kjøller, et autres
Publié: (2025)

Manifold Learning with Normalizing Flows: Towards Regularity, Expressivity and Iso-Riemannian Geometry
par: Diepeveen, Willem, et autres
Publié: (2025)

Riemannian Variational Flow Matching for Material and Protein Design
par: Zaghen, Olga, et autres
Publié: (2025)

Hardness of Learning Neural Networks under the Manifold Hypothesis
par: Kiani, Bobak T., et autres
Publié: (2024)

Cartan moving frames and the data manifolds
par: Tron, Eliot, et autres
Publié: (2024)

Kernel-Gradient Drifting Models
par: Esteban-Casadevall, Maria, et autres
Publié: (2026)

Discretized Gradient Flow for Manifold Learning in the Space of Embeddings
par: Gold, Dara, et autres
Publié: (2019)

Minimising Willmore Energy via Neural Flow
par: Hirst, Edward, et autres
Publié: (2026)

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

Steerable Neural ODEs on Homogeneous Spaces
par: Andersdotter, Emma, et autres
Publié: (2026)

Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds
par: Sun, Xingzhi, et autres
Publié: (2024)

Complex normalizing flows can almost be information Kähler-Ricci flows
par: Gracyk, Andrew
Publié: (2026)

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

Persistent de Rham-Hodge Laplacians in Eulerian representation for manifold topological learning
par: Su, Zhe, et autres
Publié: (2024)

Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows
par: Diepeveen, Willem, et autres
Publié: (2024)

Logifold: A Geometrical Foundation of Ensemble Machine Learning
par: Jung, Inkee, et autres
Publié: (2024)

Revealing Decurve Flows for Generalized Graph Propagation
par: Lin, Chen, et autres
Publié: (2024)

Thinner Latent Spaces: Detecting Dimension and Imposing Invariance with Conformal Autoencoders
par: Kevrekidis, George A., et autres
Publié: (2024)