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Found 4 papers, 1 papers with code
Causal machine learning for single-cell genomics
Advances in single-cell omics allow for unprecedented insights into the transcription profiles of individual cells.
Conditionally Invariant Representation Learning for Disentangling Cellular Heterogeneity
Specifically, the proposed approach helps to disentangle biological signals from data biases that are unrelated to the target task or the causal explanation of interest.
Sparsity in Continuous-Depth Neural Networks
Neural Ordinary Differential Equations (NODEs) have proven successful in learning dynamical systems in terms of accurately recovering the observed trajectories.
Beyond Predictions in Neural ODEs: Identification and Interventions
Spurred by tremendous success in pattern matching and prediction tasks, researchers increasingly resort to machine learning to aid original scientific discovery.
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