Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Xupeng Miao"'
Autor:
Xupeng Miao, Wentao Zhang, Yuezihan Jiang, Fangcheng Fu, Yingxia Shao, Lei Chen, Yangyu Tao, Gang Cao, Bin Cui
Publikováno v:
The VLDB Journal. 32:717-736
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::616a9bd8c739afc724b28957b51d7033
https://doi.org/10.1007/s00778-022-00768-8
https://doi.org/10.1007/s00778-022-00768-8
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 34:4119-4132
Wide models such as generalized linear models and factorization-based models have been extensively used in various predictive applications, e.g., recommendation systems. Due to the memory bounded property of the models, the performance improvement on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3d6407e7713e52cf1edaf2c313f11069
https://doi.org/10.1109/tkde.2020.3038109
https://doi.org/10.1109/tkde.2020.3038109
Publikováno v:
Science China Information Sciences. 66
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6fb3d85ad41d98067cd1c715427c677c
https://doi.org/10.1007/s11432-022-3581-9
https://doi.org/10.1007/s11432-022-3581-9
Publikováno v:
Proceedings of the 31st ACM International Conference on Information & Knowledge Management.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::851ab3aaf2c6f871dfc38e96895d5dcd
https://doi.org/10.1145/3511808.3557443
https://doi.org/10.1145/3511808.3557443
Publikováno v:
Proceedings of the 2022 International Conference on Management of Data.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::062d1167ccf7ff259e0123b5d9cc3427
https://doi.org/10.1145/3514221.3517902
https://doi.org/10.1145/3514221.3517902
Publikováno v:
The VLDB Journal. 30:769-797
Second-order random walk is an important technique for graph analysis. Many applications including graph embedding, proximity measure and community detection use it to capture higher-order patterns in the graph, thus improving the model accuracy. How
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e8577ed5eb5d97b17d571c44f9d4a43d
https://doi.org/10.1007/s00778-021-00669-2
https://doi.org/10.1007/s00778-021-00669-2
Publikováno v:
2022 IEEE 38th International Conference on Data Engineering (ICDE).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0384cdebc039ea0d82e3c79c45cb9032
https://doi.org/10.1109/icde53745.2022.00157
https://doi.org/10.1109/icde53745.2022.00157
Publikováno v:
2022 IEEE 38th International Conference on Data Engineering (ICDE).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d220063f6511852aaec9927090b9d89
https://doi.org/10.1109/icde53745.2022.00241
https://doi.org/10.1109/icde53745.2022.00241
Publikováno v:
2022 IEEE 38th International Conference on Data Engineering (ICDE).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9f4edd412fd64e1288781dddb87199ea
https://doi.org/10.1109/icde53745.2022.00177
https://doi.org/10.1109/icde53745.2022.00177
Embedding models have been an effective learning paradigm for high-dimensional data. However, one open issue of embedding models is that their representations (latent factors) often result in large parameter space. We observe that existing distribute
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::037f1ed1e120c783bcb2b9c57f49bdf5
http://arxiv.org/abs/2112.07221
http://arxiv.org/abs/2112.07221
