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Learning in Imperfect Environment: Multi-Label Classification with Long-Tailed Distribution and Partial Labels
Conventional multi-label classification (MLC) methods assume that all samples are fully labeled and identically distributed.
Toward Cohort Intelligence: A Universal Cohort Representation Learning Framework for Electronic Health Record Analysis
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.
CAusal and collaborative proxy-tasKs lEarning for Semi-Supervised Domain Adaptation
In this paper, we analyze the SSDA problem from two perspectives that have previously been overlooked, and correspondingly decompose it into two \emph{key subproblems}: \emph{robust domain adaptation (DA) learning} and \emph{maximal cross-domain data utilization}.
From Plate to Prevention: A Dietary Nutrient-aided Platform for Health Promotion in Singapore
In this paper, we share our experience in addressing this issue and attaining medical-grade nutrient intake information to benefit Singaporeans in different aspects.
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