Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Phung Lai"'
Autor:
Pelin Ayranci, Phung Lai, Nhathai Phan, Han Hu, Alexander Kolinowski, David Newman, Deijing Dou
Publikováno v:
Journal of Combinatorial Optimization. 44:770-793
Autor:
Phung Lai, NhatHai Phan, Tong Sun, Rajiv Jain, Franck Dernoncourt, Jiuxiang Gu, Nikolaos Barmpalios
In this paper, we introduce a novel concept of user-entity differential privacy (UeDP) to provide formal privacy protection simultaneously to both sensitive entities in textual data and data owners in learning natural language models (NLMs). To prese
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff2a60bc82a428a1e7967d72d0b64c17
http://arxiv.org/abs/2211.01141
http://arxiv.org/abs/2211.01141
Publikováno v:
Communications in Computer and Information Science ISBN: 9783030923099
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::01fcd8b97edd9c62c0cd92f5a62de173
https://doi.org/10.1007/978-3-030-92310-5_39
https://doi.org/10.1007/978-3-030-92310-5_39
Publikováno v:
IJCNN
In this paper, we introduce a novel interpreting framework that learns an interpretable model based on an ontology-based sampling technique to explain agnostic prediction models. Different from existing approaches, our algorithm considers contextual
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::98100430cde675507c80f6b160c465c6
Autor:
Xuan-Phung, Lai
Syftet med studien är att undersöka hur lärare och elevassistenter på gymnasieskolan kan stödja och undervisa elever med svår synnedsättning eller blindhet i matematik. Studiens fokus ligger på om anpassade läromedel och undervisningsstrateg
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-27537
Publikováno v:
SSP
In this paper, we consider the use of convolutive matrix decomposition for matrix data classification. Matrix decomposition has been broadly used as means of dimensionality reduction in a variety of learning tasks. In this approach, columns of a matr
Publikováno v:
SSP
Logistic regression is a statistical model widely used for solving classification problems. Maximum likelihood is used train the model parameters. When data from two classes is linearly separable, maximum likelihood is ill-posed. To address this prob