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Found 13 papers, 6 papers with code
Reducing operator complexity in Algebraic Multigrid with Machine Learning Approaches
We propose a data-driven and machine-learning-based approach to compute non-Galerkin coarse-grid operators in algebraic multigrid (AMG) methods, addressing the well-known issue of increasing operator complexity.
MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model
Due to patient privacy protection concerns, machine learning research in healthcare has been undeniably slower and limited than in other application domains.
MuG: A Multimodal Classification Benchmark on Game Data with Tabular, Textual, and Visual Fields
Previous research has demonstrated the advantages of integrating data from multiple sources over traditional unimodal data, leading to the emergence of numerous novel multimodal applications.
Data-Driven Linear Complexity Low-Rank Approximation of General Kernel Matrices: A Geometric Approach
Such rectangular kernel matrices may arise, for example, in Gaussian process regression where $X$ corresponds to the training data and $Y$ corresponds to the test data.
An Efficient Nonlinear Acceleration method that Exploits Symmetry of the Hessian
Nonlinear acceleration methods are powerful techniques to speed up fixed-point iterations.
AUTM Flow: Atomic Unrestricted Time Machine for Monotonic Normalizing Flows
AUTM offers a versatile and efficient way to the design of normalizing flows with explicit inverse and unrestricted function classes or parameters.
GDA-AM: On the effectiveness of solving minimax optimization via Anderson Acceleration
We also empirically show that GDA-AMsolves a variety of minimax problems and improves GAN training on several datasets
GDA-AM: ON THE EFFECTIVENESS OF SOLVING MIN-IMAX OPTIMIZATION VIA ANDERSON MIXING
We also empirically show that GDA-AM solves a variety of minimax problems and improves GAN training on several datasets
Learning optimal multigrid smoothers via neural networks
Multigrid methods are one of the most efficient techniques for solving linear systems arising from Partial Differential Equations (PDEs) and graph Laplacians from machine learning applications.
Generating a Doppelganger Graph: Resembling but Distinct
Deep generative models, since their inception, have become increasingly more capable of generating novel and perceptually realistic signals (e. g., images and sound waves).
A non-perturbative approach to computing seismic normal modes in rotating planets
A Continuous Galerkin method-based approach is presented to compute the seismic normal modes of rotating planets.
Computational Physics Earth and Planetary Astrophysics Geophysics 86-08, 86-04, 85-04, 85-08, 85-10, 15A18, 65N25, 65N30
The Eigenvalues Slicing Library (EVSL): Algorithms, Implementation, and Software
This paper describes a software package called EVSL (for EigenValues Slicing Library) for solving large sparse real symmetric standard and generalized eigenvalue problems.
Numerical Analysis
SMASH: Structured matrix approximation by separation and hierarchy
This paper presents an efficient method to perform Structured Matrix Approximation by Separation and Hierarchy (SMASH), when the original dense matrix is associated with a kernel function.
Numerical Analysis
