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Towards Neural Sparse Linear Solvers
We propose neural sparse linear solvers, a deep learning framework to learn approximate solvers for sparse symmetric linear systems.
StAnD: A Dataset of Linear Static Analysis Problems
To the best of our knowledge, this is the largest dataset for static analysis problems and it is the first public dataset of sparse linear systems (containing both the matrix and a realistic constant term).
Model-centric Data Manifold: the Data Through the Eyes of the Model
We discover that deep ReLU neural network classifiers can see a low-dimensional Riemannian manifold structure on data.
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