Zobrazeno 1 - 10
of 28
pro vyhledávání: '"KEE SIONG NG"'
Autor:
YANG LI, PURCELL, MICHAEL, RAKOTOARIVELO, THIERRY, SMITH, DAVID, RANBADUGE, THILINA, KEE SIONG NG
Publikováno v:
ACM Computing Surveys; Nov2023, Vol. 55 Issue 11, p1-39, 39p
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031200496
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::11d67448fd1b3acdd303b38b448bf69c
https://doi.org/10.1007/978-3-031-20050-2_8
https://doi.org/10.1007/978-3-031-20050-2_8
Autor:
Yang Li, Michael Purcell, Thierry Rakotoarivelo, David Smith, Thilina Ranbaduge, Kee Siong Ng
The application of graph analytics to various domains has yielded tremendous societal and economical benefits in recent years. However, the increasingly widespread adoption of graph analytics comes with a commensurate increase in the need to protect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::980b12f3ec2292ada8508bf33c2331c9
Autor:
Lingjuan Lyu, Jiong Jin, Jiangshan Yu, Karthik Nandakumar, Han Yu, Xingjun Ma, Yitong Li, Kee Siong Ng
The current standalone deep learning framework tends to result in overfitting and low utility. This problem can be addressed by either a centralized framework that deploys a central server to train a global model on the joint data from all parties, o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c9eaa8a0989c9dd31c07e707fd6d8240
http://arxiv.org/abs/1906.01167
http://arxiv.org/abs/1906.01167
Publikováno v:
SMC
In privacy-preserving machine learning, individual parties are reluctant to share their sensitive training data due to privacy concerns. Even the trained model parameters or prediction can pose serious privacy leakage. To address these problems, we d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::343f49549282a5209d3c5dce431bfe1e
Publikováno v:
CIKM
Accurate and efficient entity resolution is an open challenge of particular relevance to intelligence organisations that collect large datasets from disparate sources with differing levels of quality and standard. Starting from a first-principles for
Publikováno v:
Communications in Computer and Information Science ISBN: 9789811302916
AusDM
AusDM
Accurate and efficient record linkage is an open challenge of particular relevance to Australian Government Agencies, who recognise that so-called wicked social problems are best tackled by forming partnerships founded on large-scale data fusion. Nam
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4bed822efc5a181e72bc79916341410a
https://doi.org/10.1007/978-981-13-0292-3_7
https://doi.org/10.1007/978-981-13-0292-3_7
Autor:
Aleksander Gorajek, Xixuan Feng, Daisy Zhe Wang, Kee Siong Ng, Arun Kumar, Caleb E. Welton, Christopher Ré, Kun Li, Florian Schoppmann, Joseph M. Hellerstein, Eugene Fratkin
Publikováno v:
Proceedings of the VLDB Endowment. 5:1700-1711
MADlib is a free, open-source library of in-database analytic methods. It provides an evolving suite of SQL-based algorithms for machine learning, data mining and statistics that run at scale within a database engine, with no need for data import/exp
Publikováno v:
Journal of Artificial Intelligence Research. 40:95-142
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. Our approach is based on a direct approximation of AIXI, a Bayesian optimality notion for general reinforcement learning agents. Previously
Autor:
Kee Siong Ng, John W. Lloyd
Publikováno v:
Autonomous Agents and Multi-Agent Systems. 23:224-272
This paper introduces the execution model of a declarative programming language intended for agent applications. Features supported by the language include functional and logic programming idioms, higher-order functions, modal computation, probabilis