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20162026
most citedThe Cost-Accuracy Trade-Off In Operator Learning With Neural Networks

7 citations · 38 across the 41 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

math.AP2024

Exact Boundary Controllability for Reduced System Associated to Extended Maxwell Systems

Maarten V. de Hoop, Ching-Lung Lin, Gen Nakamura

In the theory of viscoelasticity, an important class of models admits a representation in terms of springs and dashpots. Widely used members of this class are the Maxwell model and…

cs.CL2024★ 2 cited

Transformers are Universal In-context Learners

Takashi Furuya, Maarten V. de Hoop, Gabriel Peyré

Transformers are deep architectures that define "in-context mappings" which enable predicting new tokens based on a given set of tokens (such as a prompt in NLP applications or a s…

math.AP2024★ 1 cited

Anisotropic extended Burgers model, its relaxation tensor and properties of the associated Boltzmann viscoelastic system

Maarten de Hoop, Masato Kimura, Ching-Lung Lin +2

We provide a new method for constructing the anisotropic relaxation tensor and proving its exponential decay property for the extended Burgers model (abbreviated by EBM). The EBM i…

math.AP2024

Weyl formulae for some singular metrics with application to acoustic modes in gas giants

Yves Colin de Verdìère, Charlotte Dietze, Maarten V. de Hoop +1

This paper is motivated by recent works on inverse problems for acoustic wave propagation in the interior of gas giant planets. In such planets, the speed of sound is isotropic and…

stat.ML2024★ 2 cited

Taming Score-Based Diffusion Priors for Infinite-Dimensional Nonlinear Inverse Problems

Lorenzo Baldassari, Ali Siahkoohi, Josselin Garnier +2

This work introduces a sampling method capable of solving Bayesian inverse problems in function space. It does not assume the log-concavity of the likelihood, meaning that it is co…

stat.ML2024

An Unconditional Representation of the Conditional Score in Infinite-Dimensional Linear Inverse Problems

Fabian Schneider, Duc-Lam Duong, Matti Lassas +2

Score-based diffusion models (SDMs) have emerged as a powerful tool for sampling from the posterior distribution in Bayesian inverse problems. However, existing methods often requi…