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09/12/2023
From low-rank retractions to dynamical low-rank approximation and back
In algorithms for solving optimization problems constrained to a smooth ...
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06/02/2023
Robust low-rank training via approximate orthonormal constraints
With the growth of model and data sizes, a broad effort has been made to...
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05/30/2023
Rank-adaptive spectral pruning of convolutional layers during training
The computing cost and memory demand of deep learning pipelines have gro...
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04/12/2023
A parallel rank-adaptive integrator for dynamical low-rank approximation
This work introduces a parallel and rank-adaptive matrix integrator for ...
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05/26/2022
Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equations
Neural networks have achieved tremendous success in a large variety of a...
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01/25/2022
Rank-adaptive time integration of tree tensor networks
A rank-adaptive integrator for the approximate solution of high-order te...
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07/15/2021
On the stability of robust dynamical low-rank approximations for hyperbolic problems
The dynamical low-rank approximation (DLRA) is used to treat high-dimens...
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05/10/2021
Dynamical low-rank approximation for Burgers' equation with uncertainty
Quantifying uncertainties in hyperbolic equations is a source of several...
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04/12/2021
A rank-adaptive robust integrator for dynamical low-rank approximation
A rank-adaptive integrator for the dynamical low-rank approximation of m...
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10/02/2020
An unconventional robust integrator for dynamical low-rank approximation
We propose and analyse a numerical integrator that computes a low-rank a...
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02/26/2020
