5 citations · 18 across the 18 of their papers we have counts for
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From low-rank retractions to dynamical low-rank approximation and back
Axel Séguin, Gianluca Ceruti, Daniel Kressner
In algorithms for solving optimization problems constrained to a smooth manifold, retractions are a well-established tool to ensure that the iterates stay on the manifold. More rec…
Robust low-rank training via approximate orthonormal constraints
Dayana Savostianova, Emanuele Zangrando, Gianluca Ceruti +1
With the growth of model and data sizes, a broad effort has been made to design pruning techniques that reduce the resource demand of deep learning pipelines, while retaining model…
Geometry-aware training of factorized layers in tensor Tucker format
Emanuele Zangrando, Steffen Schotthöfer, Gianluca Ceruti +2
Reducing parameter redundancies in neural network architectures is crucial for achieving feasible computational and memory requirements during training and inference phases. Given…
Numerical simulations of long-range open quantum many-body dynamics with tree tensor networks
Dominik Sulz, Christian Lubich, Gianluca Ceruti +2
Open quantum systems provide a conceptually simple setting for the exploration of collective behavior stemming from the competition between quantum effects, many-body interactions,…
A parallel rank-adaptive integrator for dynamical low-rank approximation
Gianluca Ceruti, Jonas Kusch, Christian Lubich
This work introduces a parallel and rank-adaptive matrix integrator for dynamical low-rank approximation. The method is related to the previously proposed rank-adaptive basis updat…