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Found 4 papers, 2 papers with code
A block-sparse Tensor Train Format for sample-efficient high-dimensional Polynomial Regression
Low-rank tensors are an established framework for high-dimensional least-squares problems.
Numerical Solution of the Parametric Diffusion Equation by Deep Neural Networks
Here, approximation theory predicts that the performance of the model should depend only very mildly on the dimension of the parameter space and is determined by the intrinsic dimension of the solution manifold of the parametric partial differential equation.
A Theoretical Analysis of Deep Neural Networks and Parametric PDEs
We derive upper bounds on the complexity of ReLU neural networks approximating the solution maps of parametric partial differential equations.
Convergence results for projected line-search methods on varieties of low-rank matrices via Łojasiewicz inequality
The aim of this paper is to derive convergence results for projected line-search methods on the real-algebraic variety $\mathcal{M}_{\le k}$ of real $m \times n$ matrices of rank at most $k$.
