Cover Image

Solving All Regression Models For Learning Gaussian Networks Using Givens Rotations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Alipourfard, Borzou, Gao, Jean X.
Format:	Preprint
Published:	2019
Subjects:	Numerical Analysis Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Renormalized Normalized Maximum Likelihood and Three-Part Code Criteria For Learning Gaussian Networks
by: Alipourfard, Borzou, et al.
Published: (2018)

Mode-Shape Expansion Using Physics-Constrained Gaussian Process Regression
by: Ghahari, Farid
Published: (2026)

Tensor Completion with Nearly Linear Samples Given Weak Side Information
by: Yu, Christina Lee, et al.
Published: (2020)

Solving Inverse Parametrized Problems via Finite Elements and Extreme Learning Networks
by: Burman, Erik, et al.
Published: (2026)

Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
by: Pförtner, Marvin, et al.
Published: (2022)

Deep Learning for Subspace Regression
by: Fanaskov, Vladimir, et al.
Published: (2025)

Learning to Solve Related Linear Systems
by: Hegde, Disha, et al.
Published: (2025)

Understanding the Difficulty of Solving Cauchy Problems with PINNs
by: Wang, Tao, et al.
Published: (2024)

A Gaussian Process Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
by: Mora, Carlos, et al.
Published: (2024)

Solving PDEs on Unknown Manifolds with Machine Learning
by: Liang, Senwei, et al.
Published: (2021)

Solving Nonlinear PDEs with Sparse Radial Basis Function Networks
by: Shao, Zihan, et al.
Published: (2025)

TINNs: Time-Induced Neural Networks for Solving Time-Dependent PDEs
by: Dai, Chen-Yang, et al.
Published: (2026)

Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations
by: Chen, Chuqi, et al.
Published: (2024)

Multi-Level GNN Preconditioner for Solving Large Scale Problems
by: Nastorg, Matthieu, et al.
Published: (2024)

Solving Partial Differential Equations with Random Feature Models
by: Liao, Chunyang
Published: (2024)

Solving the Wide-band Inverse Scattering Problem via Equivariant Neural Networks
by: Zhang, Borong, et al.
Published: (2022)

An End-to-End Deep Learning Method for Solving Nonlocal Allen-Cahn and Cahn-Hilliard Phase-Field Models
by: Geng, Yuwei, et al.
Published: (2024)

Gaussian Process Regression under Computational and Epistemic Misspecification
by: Sanz-Alonso, Daniel, et al.
Published: (2023)

Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural Networks
by: Baptista, Ricardo, et al.
Published: (2025)

Solving Forward and Inverse Problems of Contact Mechanics using Physics-Informed Neural Networks
by: Sahin, T., et al.
Published: (2023)

Understanding In-Context Learning for Nonlinear Regression with Transformers: Attention as Featurizer
by: Hsu, Alexander, et al.
Published: (2026)

From Simple to Complex: Curriculum-Guided Physics-Informed Neural Networks via Gaussian Mixture Models
by: Yang, Jianan, et al.
Published: (2026)

Data-Guided Physics-Informed Neural Networks for Solving Inverse Problems in Partial Differential Equations
by: Zhou, Wei, et al.
Published: (2024)

Sketching the Heat Kernel: Using Gaussian Processes to Embed Data
by: Gilbert, Anna C., et al.
Published: (2024)

Variational (Energy-Based) Spectral Learning: A Machine Learning Framework for Solving Partial Differential Equations
by: Hammad, M. M.
Published: (2026)

Modeling Large-Scale Walking and Cycling Networks: A Machine Learning Approach Using Mobile Phone and Crowdsourced Data
by: Saberi, Meead, et al.
Published: (2024)

Solving the 2D Advection-Diffusion Equation using Fixed-Depth Symbolic Regression and Symbolic Differentiation without Expression Trees
by: Finkelstein, Edward
Published: (2024)

Back-Projection Diffusion: Solving the Wideband Inverse Scattering Problem with Diffusion Models
by: Zhang, Borong, et al.
Published: (2024)

Stabilizing and Solving Unique Continuation Problems by Parameterizing Data and Learning Finite Element Solution Operators
by: Burman, Erik, et al.
Published: (2024)

DeltaPhi: Physical States Residual Learning for Neural Operators in Data-Limited PDE Solving
by: Yue, Xihang, et al.
Published: (2024)

A Physics-Informed Machine Learning Approach for Solving Distributed Order Fractional Differential Equations
by: Aghaei, Alireza Afzal
Published: (2024)

A Probabilistic Framework for Solving High-Frequency Helmholtz Equations via Diffusion Models
by: Zou, Yicheng, et al.
Published: (2026)

Sketch 'n Solve: An Efficient Python Package for Large-Scale Least Squares Using Randomized Numerical Linear Algebra
by: Lavaee, Alex
Published: (2024)

Exact Gaussian Moment Matching for Residual Networks: a Second-Order Method
by: Kuang, Simon, et al.
Published: (2026)

Gradient Flow Based Phase-Field Modeling Using Separable Neural Networks
by: Mattey, Revanth, et al.
Published: (2024)

FBSJNN: A Theoretically Interpretable and Efficiently Deep Learning method for Solving Partial Integro-Differential Equations
by: Ye, Zaijun, et al.
Published: (2024)

The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
by: Adcock, Ben, et al.
Published: (2024)

SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition
by: Gao, Yihang, et al.
Published: (2022)

Deep Parallel Spectral Neural Operators for Solving Partial Differential Equations with Enhanced Low-Frequency Learning Capability
by: Ma, Qinglong, et al.
Published: (2024)

Positional Knowledge is All You Need: Position-induced Transformer (PiT) for Operator Learning
by: Chen, Junfeng, et al.
Published: (2024)