Zobrazeno 1 - 10
of 237
pro vyhledávání: '"Sobolevsky, Stanislav"'
Autor:
Hamann, Hendrik F., Brunschwiler, Thomas, Gjorgiev, Blazhe, Martins, Leonardo S. A., Puech, Alban, Varbella, Anna, Weiss, Jonas, Bernabe-Moreno, Juan, Massé, Alexandre Blondin, Choi, Seong, Foster, Ian, Hodge, Bri-Mathias, Jain, Rishabh, Kim, Kibaek, Mai, Vincent, Mirallès, François, De Montigny, Martin, Ramos-Leaños, Octavio, Suprême, Hussein, Xie, Le, Youssef, El-Nasser S., Zinflou, Arnaud, Belvi, Alexander J., Bessa, Ricardo J., Bhattari, Bishnu Prasad, Schmude, Johannes, Sobolevsky, Stanislav
Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets through self-supervision. The resulting rich representations of complex syst
Externí odkaz:
http://arxiv.org/abs/2407.09434
We consider a variant of the clustering problem for a complete weighted graph. The aim is to partition the nodes into clusters maximizing the sum of the edge weights within the clusters. This problem is known as the clique partitioning problem, being
Externí odkaz:
http://arxiv.org/abs/2110.05627
Autor:
Sobolevsky, Stanislav
Over the recent years, Graph Neural Networks have become increasingly popular in network analytic and beyond. With that, their architecture noticeable diverges from the classical multi-layered hierarchical organization of the traditional neural netwo
Externí odkaz:
http://arxiv.org/abs/2105.03388
Autor:
Lei, Zengxiang, Xue, Jiawei, Chen, Xiaowei, Qian, Xinwu, Saumya, Charitha, He, Mingyi, Sobolevsky, Stanislav, Kulkarni, Milind, Ukkusuri, Satish V.
Publikováno v:
In Simulation Modelling Practice and Theory April 2024 132
Autor:
Sobolevsky, Stanislav
Network community detection often relies on optimizing partition quality functions, like modularity. This optimization appears to be a complex problem traditionally relying on discrete heuristics. And although the problem could be reformulated as con
Externí odkaz:
http://arxiv.org/abs/2103.02520
Digital sensing provides an unprecedented opportunity to assess and understand mobility. However, incompleteness, missing information, possible inaccuracies, and temporal heterogeneity in the geolocation data can undermine its applicability. As mobil
Externí odkaz:
http://arxiv.org/abs/2101.09844
Understanding holistic impact of planned transportation solutions and interventions on urban systems is challenged by their complexity but critical for decision making. The cornerstone for such impact assessments is estimating the transportation mode
Externí odkaz:
http://arxiv.org/abs/2010.06588
Autor:
Muaz, Urwa, Sobolevsky, Stanislav
Unsupervised anomaly detection from high dimensional data like mobility networks is a challenging task. Study of different approaches of feature engineering from such high dimensional data have been a focus of research in this field. This study aims
Externí odkaz:
http://arxiv.org/abs/1912.02864
Autor:
He, Mingyi, Pathak, Shivam, Muaz, Urwa, Zhou, Jingtian, Saini, Saloni, Malinchik, Sergey, Sobolevsky, Stanislav
Broad spectrum of urban activities including mobility can be modeled as temporal networks evolving over time. Abrupt changes in urban dynamics caused by events such as disruption of civic operations, mass crowd gatherings, holidays and natural disast
Externí odkaz:
http://arxiv.org/abs/1912.01960
Publikováno v:
In Journal of Transport Geography December 2023 113