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Found 5 papers, 2 papers with code
Revisiting Sanity Checks for Saliency Maps
They argue that some popular saliency methods should not be used for explainability purposes since the maps they produce are not sensitive to the underlying model that is to be explained.
On Calibration and Out-of-domain Generalization
In this work, we draw a link between OOD performance and model calibration, arguing that calibration across multiple domains can be viewed as a special case of an invariant representation leading to better OOD generalization.
Robust Learning with the Hilbert-Schmidt Independence Criterion
We adapt it to the task of learning for unsupervised covariate shift: learning on a source domain without access to any instances or labels from the unknown target domain, but with the assumption that $p(y|x)$ (the conditional probability of labels given instances) remains the same in the target domain.
Learning to Optimize Multigrid PDE Solvers
Constructing fast numerical solvers for partial differential equations (PDEs) is crucial for many scientific disciplines.
Improved Training for Self-Training by Confidence Assessments
Our suggested approaches were applied on the MNIST data-set as a proof of concept for a vision classification task and on the ADE20K data-set in order to tackle the semi-supervised semantic segmentation problem.
