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07/15/2023
Reducing operator complexity in Algebraic Multigrid with Machine Learning Approaches
We propose a data-driven and machine-learning-based approach to compute ...
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06/01/2023
NLTGCR: A class of Nonlinear Acceleration Procedures based on Conjugate Residuals
This paper develops a new class of nonlinear acceleration algorithms bas...
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04/11/2023
An Adaptive Factorized Nyström Preconditioner for Regularized Kernel Matrices
The spectrum of a kernel matrix significantly depends on the parameter v...
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02/08/2023
MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model
Due to patient privacy protection concerns, machine learning research in...
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02/06/2023
MuG: A Multimodal Classification Benchmark on Game Data with Tabular, Textual, and Visual Fields
Multimodal learning has attracted the interest of the machine learning c...
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12/24/2022
Data-Driven Linear Complexity Low-Rank Approximation of General Kernel Matrices: A Geometric Approach
A general, rectangular kernel matrix may be defined as K_ij = κ(x_i,y_j)...
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10/22/2022
An Efficient Nonlinear Acceleration method that Exploits Symmetry of the Hessian
Nonlinear acceleration methods are powerful techniques to speed up fixed...
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06/05/2022
AUTM Flow: Atomic Unrestricted Time Machine for Monotonic Normalizing Flows
Nonlinear monotone transformations are used extensively in normalizing f...
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06/04/2022
Data-driven Construction of Hierarchical Matrices with Nested Bases
Hierarchical matrices provide a powerful representation for significantl...
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05/04/2022
parGeMSLR: A Parallel Multilevel Schur Complement Low-Rank Preconditioning and Solution Package for General Sparse Matrices
This paper discusses parGeMSLR, a C++/MPI software library for the solut...
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10/06/2021
Solve Minimax Optimization by Anderson Acceleration
Many modern machine learning algorithms such as generative adversarial n...
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03/08/2021
Fast randomized non-Hermitian eigensolver based on rational filtering and matrix partitioning
This paper describes a set of rational filtering algorithms to compute a...
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02/24/2021
Learning optimal multigrid smoothers via neural networks
Multigrid methods are one of the most efficient techniques for solving l...
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02/10/2021
Fast and stable deterministic approximation of general symmetric kernel matrices in high dimensions
Kernel methods are used frequently in various applications of machine le...
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01/23/2021
Generating a Doppelganger Graph: Resembling but Distinct
Deep generative models, since their inception, have become increasingly ...
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02/03/2020
A power Schur complement Low-Rank correction preconditioner for general sparse linear systems
An effective power based parallel preconditioner is proposed for general...
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01/04/2020