Our results show that our method can fit simple predictive checklists that perform well and that can easily be customized to obey a rich class of custom constraints.
In this work, we benchmark the performance of eight domain generalization methods on multi-site clinical time series and medical imaging data.
Graph neural networks (GNNs) are a class of deep models that operate on data with arbitrary topology represented as graphs.
The standard interpretation of importance-weighted autoencoders is that they maximize a tighter lower bound on the marginal likelihood than the standard evidence lower bound.
Statistical machine learning methods, especially nonparametric Bayesian methods, have become increasingly popular to infer clonal population structure of tumors.
Tumors often contain multiple subpopulations of cancerous cells defined by distinct somatic mutations.
High-throughput sequencing allows the detection and quantification of frequencies of somatic single nucleotide variants (SNV) in heterogeneous tumor cell populations.