
Recursive Estimation of a Failure Probability for a Lipschitz Function
Let g : Ω = [0, 1] d → R denote a Lipschitz function that can be evaluat...
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Optimal pointwise sampling for L^2 approximation
Given a function u∈ L^2=L^2(D,μ), where D⊂ℝ^d and μ is a measure on D, a...
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Nearoptimal approximation methods for elliptic PDEs with lognormal coefficients
This paper studies numerical methods for the approximation of elliptic P...
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Secure Optimization Through Opaque Observations
Secure applications implement software protections against sidechannel ...
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Optimal sampling and Christoffel functions on general domains
We consider the problem of reconstructing an unknown function u∈ L^2(D,μ...
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Optimal Stable Nonlinear Approximation
While it is well known that nonlinear methods of approximation can often...
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Nonlinear reduced models for state and parameter estimation
State estimation aims at approximately reconstructing the solution u to ...
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Nonlinear Methods for Model Reduction
The usual approach to model reduction for parametric partial differentia...
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MLIR: A Compiler Infrastructure for the End of Moore's Law
This work presents MLIR, a novel approach to building reusable and exten...
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State Estimation – The Role of Reduced Models
The exploration of complex physical or technological processes usually r...
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On the Representation of Partially Specified Implementations and its Application to the Optimization of Linear Algebra Kernels on GPU
Traditional optimizing compilers rely on rewrite rules to iteratively ap...
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Studying EM Pulse Effects on Superscalar Microarchitectures at ISA Level
In the area of physical attacks, systemonchip (SoC) designs have not r...
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Sequential sampling for optimal weighted least squares approximations in hierarchical spaces
We consider the problem of approximating an unknown function u∈ L^2(D,ρ)...
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An Approach for Finding Permutations Quickly: Fusion and Dimension matching
Polyhedral compilers can perform complex loop optimizations that improve...
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Tensor Comprehensions: FrameworkAgnostic HighPerformance Machine Learning Abstractions
Deep learning models with convolutional and recurrent networks are now u...
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Albert Cohen
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