Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Li, Hangjian"'
This paper proposes a generative model to detect change points in time series of graphs. The proposed framework consists of learnable prior distributions for low-dimensional graph representations and of a decoder that can generate graphs from the lat
Externí odkaz:
http://arxiv.org/abs/2404.04719
Publikováno v:
Workshop on Decision Intelligence and Analytics for Online Marketplaces, KDD 2023
Online retailers often use third-party demand-side-platforms (DSPs) to conduct offsite advertising and reach shoppers across the Internet on behalf of their advertisers. The process involves the retailer participating in instant auctions with real-ti
Externí odkaz:
http://arxiv.org/abs/2306.10476
This paper studies change point detection in time series of networks, with the Separable Temporal Exponential-family Random Graph Model (STERGM). Dynamic network patterns can be inherently complex due to dyadic and temporal dependence. Detection of t
Externí odkaz:
http://arxiv.org/abs/2303.17642
Structural learning of directed acyclic graphs (DAGs) or Bayesian networks has been studied extensively under the assumption that data are independent. We propose a new Gaussian DAG model for dependent data which assumes the observations are correlat
Externí odkaz:
http://arxiv.org/abs/1905.10848
Publikováno v:
Leida xuebao, Vol 7, Iss 2, Pp 244-253 (2018)
Synchronization is a key problem in distributed Synthetic Aperture Radar (SAR) systems. In this paper, we perform a complex mathematical deduction and then analyze the influences of time synchronization on the SAR imaging and interferometric process.
Externí odkaz:
https://doaj.org/article/6f8fd5a1f025422da144df0ad3a05c87
