no code implementations • 10 Apr 2023 • Changshuo Liu, Wenqiao Zhang, Beng Chin Ooi, James Wei Luen Yip, Lingze Zeng, Kaiping Zheng
In this paper, we propose a universal COhort Representation lEarning (CORE) framework to augment EHR utilization by leveraging the fine-grained cohort information among patients.
no code implementations • 10 Jan 2023 • Kaiping Zheng, Thao Nguyen, Jesslyn Hwei Sing Chong, Charlene Enhui Goh, Melanie Herschel, Hee Hoon Lee, Changshuo Liu, Beng Chin Ooi, Wei Wang, James Yip
In this paper, we share our experience in addressing this issue and attaining medical-grade nutrient intake information to benefit Singaporeans in different aspects.
no code implementations • 9 Sep 2021 • Lei Zhu, Zhaojing Luo, Wei Wang, Meihui Zhang, Gang Chen, Kaiping Zheng
In multimedia analysis, domain adaptation studies the problem of cross-domain knowledge transfer from a label rich source domain to a label scarce target domain, thus potentially alleviates the annotation requirement for deep learning models.
1 code implementation • 5 Jul 2021 • Shaofeng Cai, Kaiping Zheng, Gang Chen, H. V. Jagadish, Beng Chin Ooi, Meihui Zhang
The key idea is to model feature interactions with cross features selectively and dynamically, by first transforming the input features into exponential space, and then determining the interaction order and interaction weights adaptively for each cross feature.
no code implementations • 17 Oct 2020 • Zhaojing Luo, Sai Ho Yeung, Meihui Zhang, Kaiping Zheng, Lei Zhu, Gang Chen, Feiyi Fan, Qian Lin, Kee Yuan Ngiam, Beng Chin Ooi
In this paper, we identify two main challenges that arise during the deployment of machine learning pipelines, and address them with the design of versioning for an end-to-end analytics system MLCask.
no code implementations • 24 Mar 2020 • Kaiping Zheng, Shaofeng Cai, Horng Ruey Chua, Wei Wang, Kee Yuan Ngiam, Beng Chin Ooi
In high stakes applications such as healthcare and finance analytics, the interpretability of predictive models is required and necessary for domain practitioners to trust the predictions.
no code implementations • 6 Nov 2019 • Fei Yu, Feiyi Fan, Shouxu Jiang, Kaiping Zheng
In this paper, a novel group recommendation method, called attentive geo-social group recommendation, is proposed to recommend the target user with both activity locations and a group of users that may join the activities.