Zobrazeno 1 - 10
of 201
pro vyhledávání: '"Francesco, Sanna"'
Many real-world networks evolve dynamically over time and present different types of connections between nodes, often called layers. In this work, we propose a latent position model for these objects, called the dynamic multiplex random dot product g
Externí odkaz:
http://arxiv.org/abs/2410.09810
Synthetic data generation has been proven successful in improving model performance and robustness in the context of scarce or low-quality data. Using the data valuation framework to statistically identify beneficial and detrimental observations, we
Externí odkaz:
http://arxiv.org/abs/2410.00759
Models for categorical sequences typically assume exchangeable or first-order dependent sequence elements. These are common assumptions, for example, in models of computer malware traces and protein sequences. Although such simplifying assumptions le
Externí odkaz:
http://arxiv.org/abs/2407.19236
High-dimensional panels of time series arise in many scientific disciplines such as neuroscience, finance, and macroeconomics. Often, co-movements within groups of the panel components occur. Extracting these groupings from the data provides a course
Externí odkaz:
http://arxiv.org/abs/2407.13314
Publikováno v:
ICAIF '24: Proceedings of the 5th ACM International Conference on AI in Finance, 573-581 (2024)
Dynamic knowledge graphs (DKGs) are popular structures to express different types of connections between objects over time. They can also serve as an efficient mathematical tool to represent information extracted from complex unstructured data source
Externí odkaz:
http://arxiv.org/abs/2407.10909
Network point processes often exhibit latent structure that govern the behaviour of the sub-processes. It is not always reasonable to assume that this latent structure is static, and detecting when and how this driving structure changes is often of i
Externí odkaz:
http://arxiv.org/abs/2407.04138
Publikováno v:
Stat 13(1), e660 (2024)
This paper introduces graph-based mutually exciting processes (GB-MEP) to model event times in network point processes, focusing on an application to docked bike-sharing systems. GB-MEP incorporates known relationships between nodes in a graph within
Externí odkaz:
http://arxiv.org/abs/2311.00595
Data preprocessing is a crucial part of any machine learning pipeline, and it can have a significant impact on both performance and training efficiency. This is especially evident when using deep neural networks for time series prediction and classif
Externí odkaz:
http://arxiv.org/abs/2310.14720
Autor:
Passino, Francesco Sanna, Mantziou, Anastasia, Ghani, Daniyar, Thiede, Philip, Bevington, Ross, Heard, Nicholas A.
Cyber-systems are under near-constant threat from intrusion attempts. Attacks types vary, but each attempt typically has a specific underlying intent, and the perpetrators are typically groups of individuals with similar objectives. Clustering attack
Externí odkaz:
http://arxiv.org/abs/2301.02505
Publikováno v:
La Medicina del Lavoro; 2022, Vol. 113 Issue 4, p1-2, 2p
