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Found 4 papers, 1 papers with code
Feature Selection Methods for Uplift Modeling and Heterogeneous Treatment Effect
Uplift modeling is a causal learning technique that estimates subgroup-level treatment effects.
Consecutive Knowledge Meta-Adaptation Learning for Unsupervised Medical Diagnosis
In the SAP, the semantic knowledge learned from the source lesion domain is transferred to consecutive target lesion domains.
Crucial Semantic Classifier-based Adversarial Learning for Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA), which aims to explore the transferrable features from a well-labeled source domain to a related unlabeled target domain, has been widely progressed.
BAMBOO: a predictive and transferable machine learning force field framework for liquid electrolyte development
Despite the widespread applications of machine learning force field (MLFF) on solids and small molecules, there is a notable gap in applying MLFF to complex liquid electrolytes.
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