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Found 3 papers, 1 papers with code
Variance Minimization in the Wasserstein Space for Invariant Causal Prediction
Selecting powerful predictors for an outcome is a cornerstone task for machine learning.
Modeling Dynamics of Biological Systems with Deep Generative Neural Networks
DyMoN is well-suited to the idiosyncrasies of biological data, including noise, sparsity, and the lack of longitudinal measurements in many types of systems.
Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks
In order to model the dynamics of such systems given snapshot data, or local transitions, we present a deep neural network framework we call Dynamics Modeling Network or DyMoN.
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