Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Caleb Chen Cao"'
Autor:
Yating Wei, Zhiyong Wang, Zhongwei Wang, Yong Dai, Gongchang Ou, Han Gao, Haitao Yang, Yue Wang, Caleb Chen Cao, Luoxuan Weng, Jiaying Lu, Rongchen Zhu, Wei Chen
Publikováno v:
IEEE Transactions on Visualization and Computer Graphics. :1-14
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f2dc29161784e451af55e32eaf9c74ee
https://doi.org/10.1109/tvcg.2023.3243228
https://doi.org/10.1109/tvcg.2023.3243228
Autor:
Rusheng Pan, Zhiyong Wang, Yating Wei, Han Gao, Gongchang Ou, Caleb Chen Cao, Jingli Xu, Tong Xu, Wei Chen
Publikováno v:
IEEE Transactions on Visualization and Computer Graphics. :1-14
A computational graph in a deep neural network (DNN) denotes a specific data flow diagram (DFD) composed of many tensors and operators. Existing toolkits for visualizing computational graphs are not applicable when the structure is highly complicated
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::84c5c5adc6fdd502065960194ac20217
https://doi.org/10.1109/tvcg.2022.3230832
https://doi.org/10.1109/tvcg.2022.3230832
Autor:
Yi Yang, Yueyuan Zheng, Didan Deng, Jindi Zhang, Yongxiang Huang, Yumeng Yang, Janet H. Hsiao, Caleb Chen Cao
Publikováno v:
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. 10:231-242
Model explanations are generated by XAI (explainable AI) methods to help people understand and interpret machine learning models. To study XAI methods from the human perspective, we propose a human-based benchmark dataset, i.e., human saliency benchm
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e0af230d8a2ffcd9f6a7dc4fa9562bf8
https://doi.org/10.1609/hcomp.v10i1.22002
https://doi.org/10.1609/hcomp.v10i1.22002
Publikováno v:
Proceedings of the 31st ACM International Conference on Information & Knowledge Management.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::11c8e757c0da87b3d68d8a157f9966ea
https://doi.org/10.1145/3511808.3557331
https://doi.org/10.1145/3511808.3557331
Publikováno v:
Proceedings of the ACM Web Conference 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2e22a030a10cbad576ec8e42b9cc98cc
https://doi.org/10.1145/3485447.3512254
https://doi.org/10.1145/3485447.3512254
Publikováno v:
Database Systems for Advanced Applications ISBN: 9783031001222
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cdc07219f82bff2097eb36e200252414
https://doi.org/10.1007/978-3-031-00123-9_52
https://doi.org/10.1007/978-3-031-00123-9_52
Publikováno v:
KDD
It has been long debated that eXplainable AI (XAI) is an important technology for model and data exploration, validation, and debugging. To deploy XAI into actual systems, an executable and comprehensive evaluation of the quality of generated explana
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e05f0a0802ae71d1097bc86d35200a91
https://doi.org/10.1145/3447548.3467148
https://doi.org/10.1145/3447548.3467148
Publikováno v:
KDD
Deep learning has shown powerful performances in many fields, however its black-box nature hinders its further applications. In response, explainable artificial intelligence emerges, aiming to explain the predictions and behaviors of deep learning mo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a7d32afbc6ab817f093c0368398ae55
https://doi.org/10.1145/3447548.3470797
https://doi.org/10.1145/3447548.3470797
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 28:2281-2295
With the rapid development of Web 2.0 and Online To Offline (O2O) marketing model, various online e vent- b ased s ocial n etwork s (EBSNs) are getting popular. An important task of EBSNs is to facilitate the most satisfactory event-participant arran
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::187322a24e01d1660658da5b16e26fd6
https://doi.org/10.1109/tkde.2016.2565468
https://doi.org/10.1109/tkde.2016.2565468
Autor:
Han Gao, Xiao-Hui Li, Caleb Chen Cao, Shenjia Zhang, Xun Xue, Yuanyuan Gao, Yuhan Shi, Lei Chen, Luyu Qiu, Cong Wang, Wei Bai
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. :1-1
We are witnessing a fast development of Artificial Intelligence (AI), but it becomes dramatically challenging to explain AI models in the past decade. “Explanation” has a flexible philosophical concept of “satisfying the subjective curiosity fo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::069a270094b0faa138c98081760202c0
https://doi.org/10.1109/tkde.2020.2983930
https://doi.org/10.1109/tkde.2020.2983930