Zobrazeno 1 - 10
of 490
pro vyhledávání: '"YEW-SOON ONG"'
Autor:
Chin Sheng Tan, Abhishek Gupta, Yew-Soon Ong, Mahardhika Pratama, Puay Siew Tan, Siew Kei Lam
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-15 (2023)
Abstract In multi-objective optimization, it becomes prohibitively difficult to cover the Pareto front (PF) as the number of points scales exponentially with the dimensionality of the objective space. The challenge is exacerbated in expensive optimiz
Externí odkaz:
https://doaj.org/article/b3d7fa6434cc4e6ebdda9169fc77402d
Publikováno v:
IEEE Access, Vol 9, Pp 41844-41860 (2021)
This paper presents a first study on solution representation learning for inducing greater alignment and hence positive transfers between distinct multi-objective optimization tasks that bear discrepancies in their original search spaces. We first es
Externí odkaz:
https://doaj.org/article/1441ab09e07b42fe93dfa1e3806ba4aa
Publikováno v:
IEEE Access, Vol 6, Pp 60380-60395 (2018)
The title of data scientist has been described as one of the sexiest jobs of the 21st century. Numerous efforts have been made to define the job of a data scientist in a qualitative manner by, for example, listing the job functions and required skill
Externí odkaz:
https://doaj.org/article/85c2cbba730d4362af905d57fcaee185
Autor:
Andre K. Y. Low, Flore Mekki-Berrada, Abhishek Gupta, Aleksandr Ostudin, Jiaxun Xie, Eleonore Vissol-Gaudin, Yee-Fun Lim, Qianxiao Li, Yew Soon Ong, Saif A. Khan, Kedar Hippalgaonkar
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-11 (2024)
Abstract The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental
Externí odkaz:
https://doaj.org/article/0fb91db8811d4d46a6d55d2c7d7fdeed
Publikováno v:
IEEE Transactions on Cybernetics. 53:2955-2968
The performance of machine learning algorithms heavily relies on the availability of a large amount of training data. However, in reality, data usually reside in distributed parties such as different institutions and may not be directly gathered and
Publikováno v:
IEEE Internet of Things Journal. 10:5519-5529
Multispace Evolutionary Search for Large-Scale Optimization With Applications to Recommender Systems
Publikováno v:
IEEE Transactions on Artificial Intelligence. 4:107-120
Publikováno v:
IEEE Transactions on Cybernetics. 53:483-496
In dealing with the expensive multiobjective optimization problem, some algorithms convert it into a number of single-objective subproblems for optimization. At each iteration, these algorithms conduct surrogate-assisted optimization on one or multip
Publikováno v:
International Journal of Computer Vision. 130:2994-3013
Contrastive learning has recently shown immense potential in unsupervised visual representation learning. Existing studies in this track mainly focus on intra-image invariance learning. The learning typically uses rich intra-image transformations to
Publikováno v:
IEEE Transactions on Cybernetics. 52:9820-9833
Bayesian optimization (BO) is well known to be sample efficient for solving black-box problems. However, BO algorithms may get stuck in suboptimal solutions even with plenty of samples. Intrinsically, such a suboptimal problem of BO can attribute to