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pro vyhledávání: '"Voorhaar, Rik"'
Tensor trains are a versatile tool to compress and work with high-dimensional data and functions. In this work we introduce the Streaming Tensor Train Approximation (STTA), a new class of algorithms for approximating a given tensor $\mathcal T$ in th
Externí odkaz:
http://arxiv.org/abs/2208.02600
Autor:
Vandereycken, Bart, Voorhaar, Rik
This work proposes a novel general-purpose estimator for supervised machine learning (ML) based on tensor trains (TT). The estimator uses TTs to parametrize discretized functions, which are then optimized using Riemannian gradient descent under the f
Externí odkaz:
http://arxiv.org/abs/2203.04352
Autor:
Hemelsoet, Nicolas, Voorhaar, Rik
We apply the sheaf cohomology BGG method developed by the authors and Lachowska-Qi to the computation of Hochschild cohomology groups of various blocks of the small quantum group. All our computations of the center of the corresponding block agree wi
Externí odkaz:
http://arxiv.org/abs/2104.05113
Autor:
Hemelsoet, Nicolas, Voorhaar, Rik
We present a computer algorithm to explicitly compute the BGG resolution and its cohomology. We give several applications, in particular computation of various sheaf cohomology groups on flag varieties. An implementation of the algorithm is available
Externí odkaz:
http://arxiv.org/abs/1911.00871
Autor:
Voorhaar, Rik
We provide a new perspective on parallel 2-transport and principal 2-group bundles with 2-connection. We define parallel 2-transport as a 2-functor from the thin fundamental 2-groupoid to the 2-category of 2-group torsors. The definition of the 2-cat
Externí odkaz:
http://arxiv.org/abs/1811.10060
Autor:
Hemelsoet, Nicolas, Voorhaar, Rik
Publikováno v:
In Journal of Algebra 15 October 2022 608:77-105
Autor:
Hemelsoet, Nicolas, Voorhaar, Rik
Publikováno v:
In Journal of Algebra 1 March 2021 569:758-783
Akademický článek
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Autor:
Voorhaar, Rik
This thesis concerns the optimization and application of low-rank methods, with a special focus on tensor trains (TTs). In particular, we develop methods for computing TT approximations of a given tensor in a variety of low-rank formats and we show h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1e8c0dc3615b59e5f7883e5b0b2198f5