Zobrazeno 1 - 10
of 3 326
pro vyhledávání: '"Structure learning"'
Autor:
Yuxin Fan, Tingting Fu, Nikolai Izmailovich Listopad, Peng Liu, Sahil Garg, Mohammad Mehedi Hassan
Publikováno v:
Alexandria Engineering Journal, Vol 106, Iss , Pp 560-570 (2024)
The Industrial Internet of Things (IIoT) infrastructure is inherently complex, often involving a multitude of sensors and devices. Ensuring the secure operation and maintenance of these systems is increasingly critical, making anomaly detection a vit
Externí odkaz:
https://doaj.org/article/2018e6b17cc44c3abf608889f069274e
Publikováno v:
International Journal of Web Information Systems, 2024, Vol. 20, Issue 4, pp. 436-451.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IJWIS-03-2024-0087
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Causal discovery with prior knowledge is important for improving performance. We consider the incorporation of marginal causal relations, which correspond to the presence or absence of directed paths in a causal model. We propose the Margina
Externí odkaz:
https://doaj.org/article/ab6f3a6526874297aede7d1ed884e990
Publikováno v:
Frontiers in Computational Neuroscience, Vol 18 (2024)
Inspired by animal navigation strategies, we introduce a novel computational model to navigate and map a space rooted in biologically inspired principles. Animals exhibit extraordinary navigation prowess, harnessing memory, imagination, and strategic
Externí odkaz:
https://doaj.org/article/357cd2173cf04158a51126cfdeab234e
Publikováno v:
Advanced Science, Vol 11, Iss 45, Pp n/a-n/a (2024)
Abstract Embeddings derived from cell graphs hold significant potential for exploring spatial transcriptomics (ST) datasets. Nevertheless, existing methodologies rely on a graph structure defined by spatial proximity, which inadequately represents th
Externí odkaz:
https://doaj.org/article/4663ddb351e94e8d9e40431a907e86f0
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 5, Pp 6213-6229 (2024)
Abstract Bayesian networks (BNs) are highly effective in handling uncertain problems, which can assist in decision-making by reasoning with limited and incomplete information. Learning a faithful directed acyclic graph (DAG) from a large number of co
Externí odkaz:
https://doaj.org/article/a45c4d1a640e463285f4020b0ac65222
Autor:
Ramin Safaeian, Mahmoud Tabandeh
Publikováno v:
Iranian Journal of Electrical and Electronic Engineering, Vol 20, Iss 2, Pp 75-84 (2024)
Directed Acyclic Graphs stand as one of the prevailing approaches for representing causal relationships within a set of variables. With observational or interventional data, certain undirected edges within a causal DAG can be oriented. Performing int
Externí odkaz:
https://doaj.org/article/0c61d4f76dbc4efd8b27fc2b5e5d4e34
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 4, Pp 5317-5329 (2024)
Abstract Federated learning makes it possible to train a machine learning model on decentralized data. Bayesian networks are widely used probabilistic graphical models. While some research has been published on the federated learning of Bayesian netw
Externí odkaz:
https://doaj.org/article/ddb2cee0caa342cb85309bf7efc88b07
Publikováno v:
In Knowledge-Based Systems 25 November 2024 304
Autor:
Friston, Karl J. a, b, Da Costa, Lancelot a, b, c, Tschantz, Alexander b, f, ⁎, Kiefer, Alex b, Salvatori, Tommaso b, Neacsu, Victorita a, Koudahl, Magnus b, Heins, Conor b, Sajid, Noor a, Markovic, Dimitrije e, Parr, Thomas d, Verbelen, Tim b, Buckley, Christopher L. b, f
Publikováno v:
In Biological Psychology November 2024 193