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Found 3 papers, 2 papers with code
Conditional Similarity Triplets Enable Covariate-Informed Representations of Single-Cell Data
Current supervised learning approaches for computing per-sample representations are optimized based only on the outcome variable to be predicted and do not take into account clinically-relevant covariates that are likely to also be measured.
Transparent Single-Cell Set Classification with Kernel Mean Embeddings
Each profiled biological sample is thus represented by a set of hundreds of thousands of multidimensional cell feature vectors, which incurs a high computational cost to predict each biological sample's associated phenotype with machine learning models.
Exchangeable Neural ODE for Set Modeling
Reasoning over an instance composed of a set of vectors, like a point cloud, requires that one accounts for intra-set dependent features among elements.
