Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Zhengzhang Chen"'
Autor:
Junheng Hao, Lu-An Tang, Yizhou Sun, Zhengzhang Chen, Haifeng Chen, Junghwan Rhee, Zhichuan Li, Wei Wang
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783031263897
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d85b853c4cbd33cb3424385df394ca4d
https://doi.org/10.1007/978-3-031-26390-3_10
https://doi.org/10.1007/978-3-031-26390-3_10
Publikováno v:
2022 IEEE International Conference on Big Data (Big Data).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::279f0cf64ace9ee26b39eff1fde32a7d
https://doi.org/10.1109/bigdata55660.2022.10021051
https://doi.org/10.1109/bigdata55660.2022.10021051
Autor:
Yuening Li, Zhengzhang Chen, Daochen Zha, Mengnan Du, Jingchao Ni, Denghui Zhang, Haifeng Chen, Xia Hu
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bb7dba35c53534bf0ad8e74636b5d3fb
https://doi.org/10.1145/3534678.3539140
https://doi.org/10.1145/3534678.3539140
Autor:
Hui Wang, Kai Zhang, Haifeng Chen, Zhengzhang Chen, Boxiang Dong, Lu-An Tang, Zhichun Li, Ying Lin
Publikováno v:
IEEE Intelligent Systems. 36:5-13
Anomaly detection has been widely applied in modern data-driven security applications to detect abnormal events/entities that deviate from the majority. However, less work has been done in terms of detecting suspicious event sequences/paths, which ar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ccfda8f0a546d0447f867b24fed0e3ca
https://doi.org/10.1109/mis.2020.3041174
https://doi.org/10.1109/mis.2020.3041174
Autor:
Yanchi Liu, Jingchao Ni, Haifeng Chen, Xin Dong, Zhengzhang Chen, Wei Cheng, Bo Zong, Gerard de Melo, Dongjin Song
Publikováno v:
AAAI
Scopus-Elsevier
Scopus-Elsevier
Recently, recommender systems have been able to emit substantially improved recommendations by leveraging user-provided reviews. Existing methods typically merge all reviews of a given user (item) into a long document, and then process user and item
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aff8e8df997af8a81dee7c99e370187c
https://doi.org/10.1609/aaai.v34i05.6268
https://doi.org/10.1609/aaai.v34i05.6268
Autor:
Xuchao Zhang, Dongjin Song, Bo Zong, Haifeng Chen, Jingchao Ni, Zhengzhang Chen, Wei Cheng, Yanchi Liu
Publikováno v:
CIKM
In many high-stakes applications of machine learning models, outputting only predictions or providing statistical confidence is usually insufficient to gain trust from end users, who often prefer a transparent reasoning paradigm. Despite the recent e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::69a62c148c195e0239cc0c940da46c04
https://doi.org/10.1145/3459637.3482355
https://doi.org/10.1145/3459637.3482355
Publikováno v:
ACM Multimedia
Detecting abnormal activities in real-world surveillance videos is an important yet challenging task as the prior knowledge about video anomalies is usually limited or unavailable. Despite that many approaches have been developed to resolve this prob
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f396e979e96cd2496278fab0dda1178
https://doi.org/10.1145/3474085.3475693
https://doi.org/10.1145/3474085.3475693
Publikováno v:
IJCNN
With the rising concerns over privacy and fairness in machine learning, privacy-preserving fair machine learning has received tremendous attention in recent years. However, most existing fair models still need to collect sensitive demographic data, w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3e4f509b2e6a5560a8cb3c14184f2629
https://doi.org/10.1109/ijcnn52387.2021.9534017
https://doi.org/10.1109/ijcnn52387.2021.9534017
Publikováno v:
ICDE
Outlier detection is an important data mining task with numerous applications such as intrusion detection, credit card fraud detection, and video surveillance. However, given a specific task with complex data, the process of building an effective dee
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ccbadf11d73e126be65432e7e9e553cc
https://doi.org/10.1109/icde51399.2021.00210
https://doi.org/10.1109/icde51399.2021.00210
Autor:
Zhengzhang Chen, Yevgeniy Vorobeychik, Liang Tong, Dongjin Song, Jingchao Ni, Haifeng Chen, Wei Cheng
Publikováno v:
CVPR
We present FACESEC, a framework for fine-grained robustness evaluation of face recognition systems. FACESEC evaluation is performed along four dimensions of adversarial modeling: the nature of perturbation (e.g., pixel-level or face accessories), the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::10050e23e453106e6852d96da442362b