Zobrazeno 1 - 10
of 447
pro vyhledávání: '"Tijl De Bie"'
Publikováno v:
IEEE Access, Vol 12, Pp 129541-129573 (2024)
Unsupervised representation learning techniques are commonly employed to analyze high-dimensional or unstructured data. In some cases, users may have prior knowledge of the topology of the data, such as a known cluster structure or the fact that it f
Externí odkaz:
https://doaj.org/article/95141289edae474b9176210cd6627ee2
Publikováno v:
IEEE Access, Vol 12, Pp 55491-55505 (2024)
Recommender systems often face congestion, characterized by an uneven distribution in the frequency of item recommendations. The presence of congestion in recommendations is especially problematic in domains where users or items have limited availabi
Externí odkaz:
https://doaj.org/article/03faf36516064aa6895ae6e7fad1e1b8
Publikováno v:
Applied Sciences, Vol 14, Iss 8, p 3516 (2024)
Dynamic Link Prediction (DLP) addresses the prediction of future links in evolving networks. However, accurately portraying the performance of DLP algorithms poses challenges that might impede progress in the field. Importantly, common evaluation pip
Externí odkaz:
https://doaj.org/article/e9a866bc1d40441e8cc3437857aad304
Publikováno v:
IEEE Access, Vol 11, Pp 117971-117983 (2023)
Representing the nodes of continuous-time temporal graphs in a low-dimensional latent space has wide-ranging applications, from prediction to visualization. Yet, analyzing continuous-time relational data with timestamped interactions introduces uniqu
Externí odkaz:
https://doaj.org/article/52105b9fdb8f4b7dab0b81e31ccfe0f9
Publikováno v:
SoftwareX, Vol 17, Iss , Pp 100997- (2022)
In this paper we introduce EvalNE, a Python toolbox for network embedding evaluation. The main goal of EvalNE is to aid researchers and practitioners in performing consistent and reproducible evaluations of new embedding methods, replicating existing
Externí odkaz:
https://doaj.org/article/9723f94dd5d44ead881922971452d7c8
Publikováno v:
PLoS ONE, Vol 16, Iss 9, p e0256922 (2021)
The democratization of AI tools for content generation, combined with unrestricted access to mass media for all (e.g. through microblogging and social media), makes it increasingly hard for people to distinguish fact from fiction. This raises the que
Externí odkaz:
https://doaj.org/article/5beb0f90633b4e599df13bdb008fc97c
Autor:
Len Vande Veire, Tijl De Bie
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2018, Iss 1, Pp 1-21 (2018)
Abstract We present the open-source implementation of the first fully automatic and comprehensive DJ system, able to generate seamless music mixes using songs from a given library much like a human DJ does. The proposed system is built on top of seve
Externí odkaz:
https://doaj.org/article/5cd07351eb6f4beba2c26048a3fe516d
Publikováno v:
Applied Sciences, Vol 11, Iss 21, p 9884 (2021)
Data often have a relational nature that is most easily expressed in a network form, with its main components consisting of nodes that represent real objects and links that signify the relations between these objects. Modeling networks is useful for
Externí odkaz:
https://doaj.org/article/e1c90d2f4559483083ef1e1c4fca1140
Publikováno v:
Applied Sciences, Vol 11, Iss 11, p 5043 (2021)
Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, the prediction of protein–protein interactions, and the identification of hidden relationships in a cr
Externí odkaz:
https://doaj.org/article/803615b6e28247479c05a904bd755ddb
Publikováno v:
Pattern Recognition Letters, 147
PATTERN RECOGNITION LETTERS
PATTERN RECOGNITION LETTERS
The rising field of Topological Data Analysis (TDA) provides a new approach to learning from data through persistence diagrams, which are topological signatures that quantify topological properties of data in a comparable manner. For point clouds, th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c75f4dde76ed34d7bc8294a767a3feed
https://doi.org/10.1016/j.patrec.2021.03.035
https://doi.org/10.1016/j.patrec.2021.03.035