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Found 6 papers, 2 papers with code
United We Pretrain, Divided We Fail! Representation Learning for Time Series by Pretraining on 75 Datasets at Once
Actually, common belief is that multi-dataset pretraining does not work for time series!
Right on Time: Revising Time Series Models by Constraining their Explanations
Feedback on explanations in both domains is then used to constrain the model, steering it away from the annotated confounding factors.
Learning by Self-Explaining
Current AI research mainly treats explanations as a means for model inspection.
Learning to Intervene on Concept Bottlenecks
Specifically, a CB2M learns to generalize interventions to appropriate novel situations via a two-fold memory with which it can learn to detect mistakes and to reapply previous interventions.
One Explanation Does Not Fit XIL
Current machine learning models produce outstanding results in many areas but, at the same time, suffer from shortcut learning and spurious correlations.
Machines Explaining Linear Programs
In this work, we extend the attribution methods for explaining neural networks to linear programs.
