Zobrazeno 1 - 10
of 94
pro vyhledávání: '"Guibing Guo"'
Publikováno v:
IEEE Access, Vol 7, Pp 33424-33433 (2019)
Image recommendation is an essential component of the modern online image sharing applications (e.g., Flickr), aiming to provide users with interesting images for further exploration. However, most existing approaches tend to treat the image in quest
Externí odkaz:
https://doaj.org/article/bc4d73d3f3b64cefa699abcca61f0531
Publikováno v:
IEEE Access, Vol 7, Pp 186247-186259 (2019)
Sequential recommendation has been recently attracting a lot attention to suggest users with next items to interact. However, most of the traditional studies implicitly assume that users and items are independent and identically distributed (IID) and
Externí odkaz:
https://doaj.org/article/84cd99a7f4184c909070efdec43e8adb
Publikováno v:
IEEE Access, Vol 7, Pp 132279-132285 (2019)
Image recommendation plays an important role for exploring user potential interests in large-scale image sharing websites (e.g., Flickr and Instagram). Social relationships have been exploited to learn user preference, and shown their effectiveness.
Externí odkaz:
https://doaj.org/article/58aaf69e78584d35858c69d57bbdb55c
Publikováno v:
IEEE Access, Vol 6, Pp 78551-78558 (2018)
In the era of the “Internet of everything,”people and devices are interconnected and keep exchanging private information, including confidential data about personal identity, locations, payments, transactions, and so on. The privacy leakage probl
Externí odkaz:
https://doaj.org/article/b251ea5260da431e86af57d4ac5b72b2
Autor:
Xiaochen Tian, Ziyin Wang, Kevin K. Yang, Jin Su, Hanwen Du, Qiuguo Zheng, Guibing Guo, Min Yang, Fei Yang, Fajie Yuan
Predicting protein function is a longstanding challenge that has significant scientific implications. The success of amino acid sequence-based learning methods depends on the relationship between sequence, structure, and function. However, recent adv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fad48ec9c30caf44ab2eb03bcd5eb15e
https://doi.org/10.1101/2023.04.02.534383
https://doi.org/10.1101/2023.04.02.534383
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 33:694-706
Negative sampling plays an important role in ranking-based recommender models. However, most existing sampling methods cannot generate informative item pairs with positive and negative instances due to two limitations: 1) they merely treat observed i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8ee4521e651856555a45f25b91e4a3a5
https://doi.org/10.1109/tnnls.2020.3028572
https://doi.org/10.1109/tnnls.2020.3028572
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783031263866
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d44e6167970213e9f8728efd7e537ada
https://doi.org/10.1007/978-3-031-26387-3_30
https://doi.org/10.1007/978-3-031-26387-3_30
Publikováno v:
IEEE Intelligent Systems. 36:23-31
Generative Adversarial Networks(GANs) have achieved a big success in collaborative ltering(CF). However, existing GAN-based methods in CF still suffer from the high-sparsity and cold-start problems; in addition, they also undergo the issues of excess
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8b5445d71d65c604dc0d4714be5b79e1
https://doi.org/10.1109/mis.2020.3029708
https://doi.org/10.1109/mis.2020.3029708
High-throughput DNA sequencing technologies open the gate to tremendous (meta)genomic data from yet-to-be-explored microbial dark matter. However, accurately assigning protein functions to new gene sequences remains challenging. To this end, we devel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::35b2551be0a359b5d0e4538edde27d1a
https://doi.org/10.1101/2022.12.28.522150
https://doi.org/10.1101/2022.12.28.522150
Autor:
Xiangling Liu, Xinyu Yang, Linkun Ouyang, Guibing Guo, Jin Su, Ruibin Xi, Ke Yuan, Fajie Yuan
Accurately predicting the effects of mutations in cancer has the potential to improve existing treatments and identify novel therapeutic targets. In this paper, we evidence for the first time that the large-scale pre-trained protein language models (
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f5efed2ed9db3f0415c691d5a15d340d
https://doi.org/10.1101/2022.09.30.510294
https://doi.org/10.1101/2022.09.30.510294