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pro vyhledávání: '"Labiod, Lazhar"'
This paper explores an empirical approach to learn more discriminantive sentence representations in an unsupervised fashion. Leveraging semantic graph smoothing, we enhance sentence embeddings obtained from pretrained models to improve results for th
Externí odkaz:
http://arxiv.org/abs/2402.12890
A growing awareness of multi-view learning as an important component in data science and machine learning is a consequence of the increasing prevalence of multiple views in real-world applications, especially in the context of networks. In this paper
Externí odkaz:
http://arxiv.org/abs/2402.04794
A common way of partitioning graphs is through minimum cuts. One drawback of classical minimum cut methods is that they tend to produce small groups, which is why more balanced variants such as normalized and ratio cuts have seen more success. Howeve
Externí odkaz:
http://arxiv.org/abs/2402.04732
Recently, a number of works have studied clustering strategies that combine classical clustering algorithms and deep learning methods. These approaches follow either a sequential way, where a deep representation is learned using a deep autoencoder be
Externí odkaz:
http://arxiv.org/abs/1901.02291
Publikováno v:
In Neurocomputing 11 January 2022 468:464-468
Publikováno v:
In Pattern Recognition December 2020 108
Akademický článek
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Publikováno v:
In Pattern Recognition April 2017 64:386-398
Publikováno v:
International Journal of Data Science and Analytics; 20240101, Issue: Preprints p1-15, 15p
Autor:
Labiod, Lazhar, Nadif, Mohamed
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems; January 2024, Vol. 35 Issue: 1 p1439-1444, 6p