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pro vyhledávání: '"Peach, Robert L."'
Traditional models based solely on pairwise associations often fail to capture the complex statistical structure of multivariate data. Existing approaches for identifying information shared among groups of $d>3$ variables are frequently computational
Externí odkaz:
http://arxiv.org/abs/2408.07533
Autor:
Peach, Robert L., Vinao-Carl, Matteo, Grossman, Nir, David, Michael, Mallas, Emma, Sharp, David, Malhotra, Paresh A., Vandergheynst, Pierre, Gosztolai, Adam
Gaussian processes (GPs) are popular nonparametric statistical models for learning unknown functions and quantifying the spatiotemporal uncertainty in data. Recent works have extended GPs to model scalar and vector quantities distributed over non-Euc
Externí odkaz:
http://arxiv.org/abs/2309.16746
Models that rely solely on pairwise relationships often fail to capture the complete statistical structure of the complex multivariate data found in diverse domains, such as socio-economic, ecological, or biomedical systems. Non-trivial dependencies
Externí odkaz:
http://arxiv.org/abs/2306.00904
Autor:
Nurisso, Marco, Arnaudon, Alexis, Lucas, Maxime, Peach, Robert L., Expert, Paul, Vaccarino, Francesco, Petri, Giovanni
Simplicial Kuramoto models have emerged as a diverse and intriguing class of models describing oscillators on simplices rather than nodes. In this paper, we present a unified framework to describe different variants of these models, categorized into
Externí odkaz:
http://arxiv.org/abs/2305.17977
Multivariate time series data that capture the temporal evolution of interconnected systems are ubiquitous in diverse areas. Understanding the complex relationships and potential dependencies among co-observed variables is crucial for the accurate st
Externí odkaz:
http://arxiv.org/abs/2305.08529
Autor:
Gosztolai, Adam, Peach, Robert L., Arnaudon, Alexis, Barahona, Mauricio, Vandergheynst, Pierre
The dynamics of neuron populations commonly evolve on low-dimensional manifolds. Thus, we need methods that learn the dynamical processes over neural manifolds to infer interpretable and consistent latent representations. We introduce a representatio
Externí odkaz:
http://arxiv.org/abs/2304.03376
Autor:
Arnaudon, Alexis, Schindler, Dominik J., Peach, Robert L., Gosztolai, Adam, Hodges, Maxwell, Schaub, Michael T., Barahona, Mauricio
We present PyGenStability, a general-use Python software package that provides a suite of analysis and visualisation tools for unsupervised multiscale community detection in graphs. PyGenStability finds optimized partitions of a graph at different le
Externí odkaz:
http://arxiv.org/abs/2303.05385
Autor:
Seyfizadeh, Ali, Peach, Robert L., Tovote, Philip, Isaias, Ioannis U., Volkmann, Jens, Muthuraman, Muthuraman
Publikováno v:
In Expert Systems With Applications 1 November 2024 253
We formulate a general Kuramoto model on weighted simplicial complexes where phases oscillators are supported on simplices of any order $k$. Crucially, we introduce linear and non-linear frustration terms that are independent of the orientation of th
Externí odkaz:
http://arxiv.org/abs/2111.11073
Dimension is a fundamental property of objects and the space in which they are embedded. Yet ideal notions of dimension, as in Euclidean spaces, do not always translate to physical spaces, which can be constrained by boundaries and distorted by inhom
Externí odkaz:
http://arxiv.org/abs/2106.05368