Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Xujiang Zhao"'
Publikováno v:
Frontiers in Big Data, Vol 7 (2024)
In recent years, analyzing the explanation for the prediction of Graph Neural Networks (GNNs) has attracted increasing attention. Despite this progress, most existing methods do not adequately consider the inherent uncertainties stemming from the ran
Externí odkaz:
https://doaj.org/article/8aadfe44502846f3ac1e188d6a9797b3
Autor:
Xujiang Zhao, Xuchao Zhang, Chen Zhao, Jin-Hee Cho, Lance Kaplan, Dong Hyun Jeong, Audun Jøsang, Haifeng Chen, Feng Chen
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9ec2458906cf7a12ba25195b15739cf5
https://doi.org/10.1109/icassp49357.2023.10096305
https://doi.org/10.1109/icassp49357.2023.10096305
Publikováno v:
Proceedings of the 31st ACM International Conference on Information & Knowledge Management.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::269d917c90ac10c6f49b6f1885e8d2da
https://doi.org/10.1145/3511808.3557556
https://doi.org/10.1145/3511808.3557556
Sound Event Early Detection (SEED) is an essential task in recognizing the acoustic environments and soundscapes. However, most of the existing methods focus on the offline sound event detection, which suffers from the over-confidence issue of early-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::684253f4dc1e34efcaef807a40b359bb
Publikováno v:
Advances in Knowledge Discovery and Data Mining ISBN: 9783031059353
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d0e7c6f0d79b219e978167afc476a43
https://doi.org/10.1007/978-3-031-05936-0_41
https://doi.org/10.1007/978-3-031-05936-0_41
Recent multilingual pre-trained language models have achieved remarkable zero-shot performance, where the model is only finetuned on one source language and directly evaluated on target languages. In this work, we propose a self-learning framework th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1d2c0c39c8eddb4778efd4f848e54d90
http://arxiv.org/abs/2109.00194
http://arxiv.org/abs/2109.00194
Autor:
Latifur Khan, Xujiang Zhao, Yu Lin, Zhuoyi Wang, Yigong Wang, Hemeng Tao, Chen Zhao, Yuqiao Chen
Publikováno v:
WWW
Non-stationary data stream mining aims to classify large scale online instances that emerge continuously. The most apparent challenge compared with the offline learning manner is the issue of consecutive emergence of new categories, when tackling non
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8ecca903a00b45ba2e466fc8472aac61
https://doi.org/10.1145/3442381.3449820
https://doi.org/10.1145/3442381.3449820
Publikováno v:
IEEE BigData
Inference of unknown opinions with uncertain, adversarial (e.g., incorrect or conflicting) evidence in large datasets is not a trivial task. Without proper handling, it can easily mislead decision making in data mining tasks. In this work, we propose
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::64dbc1112ef5433f915362a64420f668
https://doi.org/10.1109/bigdata47090.2019.9006319
https://doi.org/10.1109/bigdata47090.2019.9006319
Publikováno v:
IEEE BigData
Subjective Logic (SL) is one of well-known belief models that can explicitly deal with uncertain opinions and infer unknown opinions based on a rich set of operators of fusing multiple opinions. Due to high simplicity and applicability, SL has been s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8b498c91efb95e8080db2ea5706062af
http://arxiv.org/abs/1910.05640
http://arxiv.org/abs/1910.05640