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Found 3 papers, 1 papers with code
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
Explainability for machine learning models has gained considerable attention within the research community given the importance of deploying more reliable machine-learning systems.
Beyond Trivial Counterfactual Generations with Diverse Valuable Explanations
In computer vision applications, most methods explain models by displaying the regions in the input image that they focus on for their prediction, but it is difficult to improve models based on these explanations since they do not indicate why the model fail.
Deep Autoencoders for Dimensionality Reduction of High-Content Screening Data
High-content screening uses large collections of unlabeled cell image data to reason about genetics or cell biology.
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