Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Ravdeep Pasricha"'
Publikováno v:
ICPR
In this paper11This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-000R22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledg
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::caa1d7457fd435ec3f56cbc611f63a56
https://doi.org/10.1109/icpr48806.2021.9412320
https://doi.org/10.1109/icpr48806.2021.9412320
Autor:
Amit K. Roy-Chowdhury, Ravdeep Pasricha, Evangelos E. Papalexakis, Niluthpol Chowdhury Mithun
Publikováno v:
ICPR
In this paper, we address the problem of utilizing web images in training robust joint embedding models for the image-text retrieval task. Prior webly supervised approaches directly leverage weakly annotated web images in the joint embedding learning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0e2c25982ab19b3a3f11333967f47806
https://doi.org/10.1109/icpr48806.2021.9412106
https://doi.org/10.1109/icpr48806.2021.9412106
Publikováno v:
WWW
How are communities in real multi-aspect or multi-view graphs structured? How we can effectively and concisely summarize and explore those communities in a high-dimensional, multi-aspect graph without losing important information? State-of-the-art st
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::94ccfc3c3649c592035ddf6bce6426e8
https://doi.org/10.1145/3366423.3380129
https://doi.org/10.1145/3366423.3380129
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783030109271
ECML/PKDD (2)
ECML/PKDD (2)
Tensor decompositions are used in various data mining applications from social network to medical applications and are extremely useful in discovering latent structures or concepts in the data. Many real-world applications are dynamic in nature and s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f2b20ff2542bb605da7de5744f558ecb
https://doi.org/10.1007/978-3-030-10928-8_20
https://doi.org/10.1007/978-3-030-10928-8_20
Publikováno v:
CAMSAP
Tensor decompositions are powerful tools for large data analytics, as they jointly model multiple aspects of data into one framework and enable the discovery of the latent structures and higher-order correlations within the data. One of the most wide
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3debdeefef777d0fff2e7f3eb7c3fc73
Publikováno v:
Scopus-Elsevier
Tensor decompositions are invaluable tools in analyzing multimodal datasets. In many real-world scenarios, such datasets are far from being static, to the contrary they tend to grow over time. For instance, in an online social network setting, as we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::127ad51c7526b984d9a5f08e831b2cfe