:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autore principale:	Dorrego, Gustavo
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning Functional Analysis
Accesso online:	https://arxiv.org/abs/2603.12794
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Weighted Sobolev Spaces and Distributional Spectral Theory for Generalized Aging Operators via Transmutation Methods
di: Dorrego, Gustavo
Pubblicazione: (2026)

A Unified Spectral Framework for Aging, Heterogeneous, and Distributed Order Systems via Weighted Weyl-Sonine Operators
di: Dorrego, Gustavo
Pubblicazione: (2026)

A Semigroup Approach to Weighted Weyl-Sonine Operators: Bypassing Bernstein Functions for Well-Posedness
di: Dorrego, Gustavo
Pubblicazione: (2026)

Spectral Theory of the Weighted Fourier Transform with respect to a Function in $\mathbb{R}^n$: Uncertainty Principle and Diffusion-Wave Applications
di: Dorrego, Gustavo, et al.
Pubblicazione: (2025)

Learning Operators by Regularized Stochastic Gradient Descent with Operator-valued Kernels
di: Yang, Jia-Qi, et al.
Pubblicazione: (2025)

A Kernel-based Stochastic Approximation Framework for Nonlinear Operator Learning
di: Yang, Jia-Qi, et al.
Pubblicazione: (2025)

Beyond the Markovian Assumption: Robust Optimization via Fractional Weyl Integrals in Imbalanced Data
di: Dorrego, Gustavo A.
Pubblicazione: (2026)

A Kernel-Based Approach for Modelling Gaussian Processes with Functional Information
di: Brown, D. Andrew, et al.
Pubblicazione: (2022)

Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces
di: Wang, Rui, et al.
Pubblicazione: (2023)

Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
di: Berner, Julius, et al.
Pubblicazione: (2025)

Non-Asymptotic Stability and Consistency Guarantees for Physics-Informed Neural Networks via Coercive Operator Analysis
di: Katende, Ronald
Pubblicazione: (2025)

Localisation of Regularised and Multiview Support Vector Machine Learning
di: Gheondea, Aurelian, et al.
Pubblicazione: (2023)

Approximation by Steklov Neural Network Operators
di: Karaman, S. N., et al.
Pubblicazione: (2024)

Gaussian Processes with Sample Paths in Reproducing Kernel Banach Spaces
di: Karvonen, Toni, et al.
Pubblicazione: (2026)

Smoothed Distance Kernels for MMDs and Applications in Wasserstein Gradient Flows
di: Rux, Nicolaj, et al.
Pubblicazione: (2025)

Which Spaces can be Embedded in $L_p$-type Reproducing Kernel Banach Space? A Characterization via Metric Entropy
di: Lu, Yiping, et al.
Pubblicazione: (2024)

Theory-to-Practice Gap for Neural Networks and Neural Operators
di: Grohs, Philipp, et al.
Pubblicazione: (2025)

Distribution-Free Stochastic Analysis and Robust Multilevel Vector Field Anomaly Detection
di: Castrillon-Candas, Julio E, et al.
Pubblicazione: (2022)

Quantitative Sobolev Approximation Bounds for Neural Operators with Empirical Validation on Burgers Equation
di: Hao, Nicole
Pubblicazione: (2026)

Convergence Analysis of Max-Min Exponential Neural Network Operators in Orlicz Space
di: Pradhan, Satyaranjan, et al.
Pubblicazione: (2025)

How Analysis Can Teach Us the Optimal Way to Design Neural Operators
di: Le, Vu-Anh, et al.
Pubblicazione: (2024)

Nonlocal Fourier Laws for Heat Propagation via Fractional powers of Vector Operators
di: Colombo, Fabrizio, et al.
Pubblicazione: (2026)

Statistical and Geometrical properties of regularized Kernel Kullback-Leibler divergence
di: Chazal, Clémentine, et al.
Pubblicazione: (2024)

Learning Orthonormal Bases for Function Spaces
di: Kamkari, Hamidreza, et al.
Pubblicazione: (2026)

Spherical Analysis of Learning Nonlinear Functionals
di: Yang, Zhenyu, et al.
Pubblicazione: (2024)

The Function Representation of Artificial Neural Network
di: Ma, Zhongkui
Pubblicazione: (2019)

Noisy Data Visualization using Functional Data Analysis
di: Chen, Haozhe, et al.
Pubblicazione: (2024)

Some Spectral Problems for First Order Normal Differential Operators in the Weighted Hilbert Spaces of Vector-Functions
di: Ismailov, Zameddin I., et al.
Pubblicazione: (2026)

Parseval Convolution Operators and Neural Networks
di: Unser, Michael, et al.
Pubblicazione: (2024)

Learning Operators with Stochastic Gradient Descent in General Hilbert Spaces
di: Shi, Lei, et al.
Pubblicazione: (2024)

Understanding Transfer Learning via Mean-field Analysis
di: Aminian, Gholamali, et al.
Pubblicazione: (2024)

Control Occupation Kernel Regression for Nonlinear Control-Affine Systems
di: Abudia, Moad, et al.
Pubblicazione: (2021)

Universal Approximation of Continuous Functionals on Compact Subsets via Linear Measurements and Scalar Nonlinearities
di: Krylov, Andrey, et al.
Pubblicazione: (2026)

Universal Approximation of Nonlinear Operators and Their Derivatives
di: de Feo, Filippo
Pubblicazione: (2026)

Fisher-Rao Gradient Flow: Geodesic Convexity and Functional Inequalities
di: Carrillo, José A., et al.
Pubblicazione: (2024)

Weighted fractional ultrahyperbolic diffusion on geometrically deformed domains
di: Dorrego, Gustavo
Pubblicazione: (2026)

Featured Reproducing Kernel Banach Spaces for Learning and Neural Networks
di: de la Higuera, Isabel, et al.
Pubblicazione: (2026)

Fast, Convex and Conditioned Network for Multi-Fidelity Vectors and Stiff Univariate Differential Equations
di: Rout, Siddharth
Pubblicazione: (2025)

Sparse-Aware Neural Networks for Nonlinear Functionals: Mitigating the Exponential Dependence on Dimension
di: Li, Jianfei, et al.
Pubblicazione: (2026)

Do stable neural networks exist for classification problems? -- A new view on stability in AI
di: Liu, Z. N. D., et al.
Pubblicazione: (2024)