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pro vyhledávání: '"Gallagher, Ian"'
We report experimental studies of coherent population trapping and spin relaxation in a temperature range between 4 K and 100 mK in a silicon vacancy (SiV) center subject to a transverse magnetic field. Near and below 1 K, phonon-induced spin dephasi
Externí odkaz:
http://arxiv.org/abs/2409.14856
Graph neural networks (GNNs) are powerful black-box models which have shown impressive empirical performance. However, without any form of uncertainty quantification, it can be difficult to trust such models in high-risk scenarios. Conformal predicti
Externí odkaz:
http://arxiv.org/abs/2405.19230
In this paper, we address the problem of dynamic network embedding, that is, representing the nodes of a dynamic network as evolving vectors within a low-dimensional space. While the field of static network embedding is wide and established, the fiel
Externí odkaz:
http://arxiv.org/abs/2311.09251
We present a new representation learning framework, Intensity Profile Projection, for continuous-time dynamic network data. Given triples $(i,j,t)$, each representing a time-stamped ($t$) interaction between two entities ($i,j$), our procedure return
Externí odkaz:
http://arxiv.org/abs/2306.06155
Spectral embedding finds vector representations of the nodes of a network, based on the eigenvectors of its adjacency or Laplacian matrix, and has found applications throughout the sciences. Many such networks are multipartite, meaning their nodes ca
Externí odkaz:
http://arxiv.org/abs/2202.03945
We consider the problem of embedding a dynamic network, to obtain time-evolving vector representations of each node, which can then be used to describe changes in behaviour of individual nodes, communities, or the entire graph. Given this open-ended
Externí odkaz:
http://arxiv.org/abs/2106.01282
Popular network models such as the mixed membership and standard stochastic block model are known to exhibit distinct geometric structure when embedded into $\mathbb{R}^{d}$ using spectral methods. The resulting point cloud concentrates around a simp
Externí odkaz:
http://arxiv.org/abs/1912.10238
When analyzing weighted networks using spectral embedding, a judicious transformation of the edge weights may produce better results. To formalize this idea, we consider the asymptotic behavior of spectral embedding for different edge-weight represen
Externí odkaz:
http://arxiv.org/abs/1910.05534
Publikováno v:
In Animal Behaviour June 2023 200:137-146
Autor:
Gallagher, Ian1 (AUTHOR), Jones, Andrew1 (AUTHOR), Bertiger, Anna2 (AUTHOR), Priebe, Carey E.3 (AUTHOR), Rubin-Delanchy, Patrick1 (AUTHOR) patrick.rubin-delanchy@bristol.ac.uk
Publikováno v:
Journal of the American Statistical Association. Sep2024, Vol. 119 Issue 547, p1923-1932. 10p.