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Found 4 papers, 1 papers with code
Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances
For this method, we prove that a bandit online learning algorithm -- using only the number of iterations as feedback -- can select parameters for a sequence of instances such that the overall cost approaches that of the best fixed $\omega$ as the sequence length increases.
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.
Integrating Deep Learning in Domain Sciences at Exascale
This paper discusses the necessities of an HPC deep learning framework and how those needs can be provided (e. g., as in MagmaDNN) through a deep integration with existing HPC libraries, such as MAGMA and its modular memory management, MPI, CuBLAS, CuDNN, MKL, and HIP.
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
