Image de couverture de livre

Stochastic Rounding Increases Small Singular Values

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Ma, Linkai, Yu, Tingzhou, Drineas, Petros
Format:	Preprint
Publié:	2026
Sujets:	Numerical Analysis Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Stochastic Rounding Implicitly Regularizes Tall-and-Thin Matrices
par: Dexter, Gregory, et autres
Publié: (2024)

Stochastic Rounding 2.0, with a View towards Complexity Analysis
par: Drineas, Petros, et autres
Publié: (2024)

Training with Fewer Bits: Unlocking Edge LLMs Training with Stochastic Rounding
par: Liu, Taowen, et autres
Publié: (2025)

A Note on the Stability of the Sherman-Morrison-Woodbury Formula
par: Ma, Linkai, et autres
Publié: (2025)

A Kernelizable Primal-Dual Formulation of the Multilinear Singular Value Decomposition
par: Wesel, Frederiek, et autres
Publié: (2024)

On Stochastic Rounding with Few Random Bits
par: Fitzgibbon, Andrew, et autres
Publié: (2025)

SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition
par: Gao, Yihang, et autres
Publié: (2022)

On the Convergence of the Gradient Descent Method with Stochastic Fixed-point Rounding Errors under the Polyak-Lojasiewicz Inequality
par: Xia, Lu, et autres
Publié: (2023)

Learn Singularly Perturbed Solutions via Homotopy Dynamics
par: Chen, Chuqi, et autres
Publié: (2025)

Learning Singularity-Encoded Green's Functions with Application to Iterative Methods
par: Sun, Qi, et autres
Publié: (2025)

Point Source Identification Using Singularity Enriched Neural Networks
par: Hu, Tianhao, et autres
Publié: (2024)

Component Fourier Neural Operator for Singularly Perturbed Differential Equations
par: Li, Ye, et autres
Publié: (2024)

Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches
par: Wang, Yixuan
Publié: (2026)

Analyzing dynamics and average case complexity in the spherical Sherrington-Kirkpatrick model: a focus on extreme eigenvectors
par: Yu, Tingzhou
Publié: (2024)

Radial Müntz-Szász Networks: Neural Architectures with Learnable Power Bases for Multidimensional Singularities
par: N'guessan, Gnankan Landry Regis, et autres
Publié: (2026)

Data-Driven Self-Supervised Learning for the Discovery of Solution Singularity for Partial Differential Equations
par: Cai, Difeng, et autres
Publié: (2025)

General-Kindred Physics-Informed Neural Network to the Solutions of Singularly Perturbed Differential Equations
par: Wang, Sen, et autres
Publié: (2024)

Stochastic diagonal estimation with adaptive parameter selection
par: Han, Zongyuan, et autres
Publié: (2024)

Streaming Krylov-Accelerated Stochastic Gradient Descent
par: Thomas, Stephen
Publié: (2025)

Learning Stochastic Dynamical Systems with Structured Noise
par: Guo, Ziheng, et autres
Publié: (2025)

Neural Network Approach to Stochastic Dynamics for Smooth Multimodal Density Estimation
par: Zarezadeh, Z., et autres
Publié: (2025)

Stochastic generative methods for stable and accurate closure modeling of chaotic dynamical systems
par: Williams, Emily, et autres
Publié: (2025)

Matrix-Free Least Squares Solvers: Values, Gradients, and What to Do With Them
par: Roy, Hrittik, et autres
Publié: (2025)

Stochastic Dimension Implicit Functional Projections for Exact Integral Conservation in High-Dimensional PINNs
par: Liang, Zhangyong
Publié: (2026)

Modified Equations for Stochastic Optimization
par: Perko, Stefan
Publié: (2025)

Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks
par: Jin, Bangti, et autres
Publié: (2025)

An Enhanced Zeroth-Order Stochastic Frank-Wolfe Framework for Constrained Finite-Sum Optimization
par: Ye, Haishan, et autres
Publié: (2025)

Balance-Guided Sparse Identification of Multiscale Nonlinear PDEs with Small-coefficient Terms
par: Dang, Zhenhua, et autres
Publié: (2026)

Stochastic Fractional Neural Operators: A Symmetrized Approach to Modeling Turbulence in Complex Fluid Dynamics
par: Santos, Rômulo Damasclin Chaves dos, et autres
Publié: (2025)

Learning Discontinuous Galerkin Solutions to Elliptic Problems via Small Linear Convolutional Neural Networks
par: Celaya, Adrian, et autres
Publié: (2025)

N-Adaptive Ritz Method: A Neural Network Enriched Partition of Unity for Boundary Value Problems
par: Baek, Jonghyuk, et autres
Publié: (2024)

Small singular values can increase in lower precision
par: Boutsikas, Christos, et autres
Publié: (2023)

Limited-Precision Stochastic Rounding
par: Arar, El-Mehdi El, et autres
Publié: (2026)

Machine Learning Based Optimization Workflow for Tuning Numerical Settings of Differential Equation Solvers for Boundary Value Problems
par: Victor, Viny Saajan, et autres
Publié: (2024)

How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework
par: Ren, Yinuo, et autres
Publié: (2024)

Unified Stochastic Framework for Neural Network Quantization and Pruning
par: Zhang, Haoyu, et autres
Publié: (2024)

Sparse Random-Feature Neural Networks with Krylov-Based SVD for Singularly Perturbed ODE
par: Vaidyan, Kevin Kurian Thomas, et autres
Publié: (2026)

MVNN: A Measure-Valued Neural Network for Learning McKean-Vlasov Dynamics from Particle Data
par: Lyu, Liyao, et autres
Publié: (2026)

Sampling via Stochastic Interpolants by Langevin-based Velocity and Initialization Estimation in Flow ODEs
par: Duan, Chenguang, et autres
Publié: (2026)

Deep Parallel Spectral Neural Operators for Solving Partial Differential Equations with Enhanced Low-Frequency Learning Capability
par: Ma, Qinglong, et autres
Publié: (2024)