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Found 8 papers, 4 papers with code
Memory-Based Graph Networks
Graph neural networks (GNNs) are a class of deep models that operate on data with arbitrary topology represented as graphs.
An Empirical Framework for Domain Generalization in Clinical Settings
In this work, we benchmark the performance of eight domain generalization methods on multi-site clinical time series and medical imaging data.
Learning Optimal Predictive Checklists
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.
Segmenting Hybrid Trajectories using Latent ODEs
Smooth dynamics interrupted by discontinuities are known as hybrid systems and arise commonly in nature.
Reinterpreting Importance-Weighted Autoencoders
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.
Reconstructing subclonal composition and evolution from whole genome sequencing of tumors
Tumors often contain multiple subpopulations of cancerous cells defined by distinct somatic mutations.
Comparing Nonparametric Bayesian Tree Priors for Clonal Reconstruction of Tumors
Statistical machine learning methods, especially nonparametric Bayesian methods, have become increasingly popular to infer clonal population structure of tumors.
Inferring clonal evolution of tumors from single nucleotide somatic mutations
High-throughput sequencing allows the detection and quantification of frequencies of somatic single nucleotide variants (SNV) in heterogeneous tumor cell populations.
