Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Kefato, Zekarias T."'
Self-supervised Learning (SSL) aims at learning representations of objects without relying on manual labeling. Recently, a number of SSL methods for graph representation learning have achieved performance comparable to SOTA semi-supervised GNNs. A Si
Externí odkaz:
http://arxiv.org/abs/2108.10420
Real world data is mostly unlabeled or only few instances are labeled. Manually labeling data is a very expensive and daunting task. This calls for unsupervised learning techniques that are powerful enough to achieve comparable results as semi-superv
Externí odkaz:
http://arxiv.org/abs/2103.14958
In recommender systems (RSs), predicting the next item that a user interacts with is critical for user retention. While the last decade has seen an explosion of RSs aimed at identifying relevant items that match user preferences, there is still a ran
Externí odkaz:
http://arxiv.org/abs/2011.05208
Graph representation learning (GRL) is a powerful technique for learning low-dimensional vector representation of high-dimensional and often sparse graphs. Most studies explore the structure and metadata associated with the graph using random walks a
Externí odkaz:
http://arxiv.org/abs/2004.00413
Graph embedding algorithms are used to efficiently represent (encode) a graph in a low-dimensional continuous vector space that preserves the most important properties of the graph. One aspect that is often overlooked is whether the graph is directed
Externí odkaz:
http://arxiv.org/abs/2001.11297
Network representation learning (NRL) is a powerful technique for learning low-dimensional vector representation of high-dimensional and sparse graphs. Most studies explore the structure and metadata associated with the graph using random walks and e
Externí odkaz:
http://arxiv.org/abs/2001.10394
Autor:
Stefanoni, Andrea, Girdzijauskas, Šarūnas, Jenkins, Christina, Kefato, Zekarias T., Sbattella, Licia, Vincenzo Scotti, Wåreus, Emil
Publikováno v:
Vincenzo Scotti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::2d61dedfebd811d6022fe61a102a35b6
https://aclanthology.org/2022.icnlsp-1.6
https://aclanthology.org/2022.icnlsp-1.6