WebDec 6, 2024 · Explaining the output of a complex machine learning (ML) model often requires approximation using a simpler model. To construct interpretable explanations that are also consistent with the original ML model, counterfactual examples — showing how the model's output changes with small perturbations to the input — have been proposed. WebModel agnostic generation of counterfactual explanations for molecules† Geemi P. Wellawatte,a Aditi Seshadrib and Andrew D. White *b An outstanding challenge in deep …
The Ultimate Guide to Counterfactual Explanations for Classification Models
WebAug 20, 2024 · Consistent Counterfactuals for Deep Models. ICLR2024 a service of home blog statistics browse persons conferences journals series search search dblp lookup by ID about f.a.q. team license privacy imprint manage site settings To protect your privacy, all features that rely on external API calls from your browser are turned off by default. WebOct 6, 2024 · This paper studies the consistency of model prediction on counterfactual examples in deep networks under small changes to initial training conditions, such as … pottberg accounting
Consistent Counterfactuals for Deep Models - NASA/ADS
WebConsistent Counterfactuals for Deep Models Emily Black · Zifan Wang · Matt Fredrikson Keywords: [ explainability ] [ consistency ] [ deep networks ] [ Abstract ] [ Visit Poster at … WebApr 23, 2024 · In this paper, we introduce Multi-Objective Counterfactuals (MOC) which, to the best of our knowledge, is the first method to formalize the counterfactual search as a … WebThese do not Look Like Those: An Interpretable Deep Learning Model for Image Recognition: IEEE: Correcting neural networks based on explanations: Refining Neural Networks with Compositional Explanations: ... Semantically consistent counterfactuals: Making Heads or Tails: Towards Semantically Consistent Visual Counterfactuals: Arxiv: touchscreen a grandma could use