Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Chih-Kuan Yeh"'
Autor:
Chih‐Kuan Yeh, Pradeep Ravikumar
Publikováno v:
Applied AI Letters, Vol 2, Iss 4, Pp n/a-n/a (2021)
Abstract Objective criteria to evaluate the performance of machine learning (ML) model explanations are a critical ingredient in bringing greater rigor to the field of explainable artificial intelligence. In this article, we survey three of our propo
Externí odkaz:
https://doaj.org/article/0c6d23f22d4c4605901f9151d4c259b9
Autor:
Chih-Kuan Yeh, 葉致寬
102
This paper argues the accountant experience greater than audit tenure, In audit fee. The cost savings will be back to check on lower audit fee the accountant experience longer expected more conducive to low strategies to recruit customers An
This paper argues the accountant experience greater than audit tenure, In audit fee. The cost savings will be back to check on lower audit fee the accountant experience longer expected more conducive to low strategies to recruit customers An
Externí odkaz:
http://ndltd.ncl.edu.tw/handle/y4362v
Autor:
Che-Ping Tsai1 CHEPINGT@CS.CMU.EDU, Chih-Kuan Yeh1 CJYEH@CS.CMU.EDU, Ravikumar, Pradeep1 PRADEEPR@CS.CMU.EDU
Publikováno v:
Journal of Machine Learning Research. 2023, Vol. 24, p1-42. 42p.
Understanding complex machine learning models such as deep neural networks with explanations is crucial in various applications. Many explanations stem from the model perspective, and may not necessarily effectively communicate why the model is makin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0dfc033ebf8ed04b276764b4015f9fce
https://doi.org/10.3233/faia210362
https://doi.org/10.3233/faia210362
Autor:
Pradeep Ravikumar, Chih-Kuan Yeh
Publikováno v:
Applied AI Letters, Vol 2, Iss 4, Pp n/a-n/a (2021)
Objective criteria to evaluate the performance of machine learning (ML) model explanations are a critical ingredient in bringing greater rigor to the field of explainable artificial intelligence. In this article, we survey three of our proposed crite
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c9185a1b86935da85955175e21ba20bd
https://doi.org/10.1002/ail2.57
https://doi.org/10.1002/ail2.57
Publikováno v:
IEEE Transactions on Games. 10:365-377
Bridge is among the zero-sum games for which artificial intelligence has not yet outperformed expert human players. The main difficulty lies in the bidding phase of bridge, which requires cooperative decision making with partial information. Existing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d3e1190f091d20e6494a045cd16d7681
https://doi.org/10.1109/tg.2018.2866036
https://doi.org/10.1109/tg.2018.2866036
Publikováno v:
Computer Vision – ECCV 2018 ISBN: 9783030012151
ECCV (2)
ECCV (2)
In order to train learning models for multi-label classification (MLC), it is typically desirable to have a large amount of fully annotated multi-label data. Since such annotation process is in general costly, we focus on the learning task of weakly-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d901bd5ee06e2eb0ba0953b105bae92b
https://doi.org/10.1007/978-3-030-01216-8_25
https://doi.org/10.1007/978-3-030-01216-8_25
Publikováno v:
CVPR
In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance. Inspired by the way humans utilize semantic knowledge between obj
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f21f5a12bb529743618ad3957c1a5960
http://arxiv.org/abs/1711.06526
http://arxiv.org/abs/1711.06526
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 31
Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::58cf7bec7ecb6f904c1d109ec88e122b
https://doi.org/10.1609/aaai.v31i1.10769
https://doi.org/10.1609/aaai.v31i1.10769
Publikováno v:
MMSP
In this paper, we propose a graph-based image retrieval algorithm via query and database specific feature fusion. While existing feature fusion approaches exist for image retrieval, they typically do not consider the image database of interest (i.e.,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1f6bae9c1b825fcdef3074551b31e1e1
https://doi.org/10.1109/mmsp.2016.7813337
https://doi.org/10.1109/mmsp.2016.7813337