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Found 5 papers, 3 papers with code
Effective Latent Differential Equation Models via Attention and Multiple Shooting
Scientific Machine Learning (SciML) is a burgeoning field that synergistically combines domain-aware and interpretable models with agnostic machine learning techniques.
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series
We revise the existing OOD generalization algorithms for time series tasks and evaluate them using our systematic framework.
Generative Models of Brain Dynamics -- A review
The principled design and discovery of biologically- and physically-informed models of neuronal dynamics has been advancing since the mid-twentieth century.
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization
To answer these questions, we revisit the fundamental assumptions in linear regression tasks, where invariance-based approaches were shown to provably generalize OOD.
SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalization
A major bottleneck in the real-world applications of machine learning models is their failure in generalizing to unseen domains whose data distribution is not i. i. d to the training domains.
