feature attribution

Keep CALM and Improve Visual Feature Attribution ICCV 2021 Learning to Explain: Generating Stable Explanations Fast ACL 2021 Leveraging Sparse Linear Layers for Debuggable Deep Networks ICML 2021 Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis NeurIPS 2021 Neural Additive Models: Interpretable Machine Learning with Neural Nets NeurIPS 2021 Neural Response Interpretation Through the Lens of Critical Pathways CVPR 2021 On Locality of Local Explanation Models NeurIPS 2021 On Smoother Attributions using Neural Stochastic Differential Equations IJCAI 2021 On the Tractability of SHAP Explanations AAAI 2021 Rationales for Sequential Predictions EMNLP 2021 Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions AAAI 2021 Self-Interpretable Model with Transformation Equivariant Interpretation NeurIPS 2021 The effectiveness of feature attribution methods and its correlation with automatic evaluation scores NeurIPS 2021 Thermostat: A Large Collection of NLP Model Explanations and Analysis Tools EMNLP 2021 Towards Better Explanations of Class Activation Mapping ICCV 2021 Towards Rigorous Interpretations: a Formalisation of Feature Attribution ICML 2021 What does LIME really see in images? ICML 2021 A Diagnostic Study of Explainability Techniques for Text Classification EMNLP 2020 AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models JMLR 2020 A Survey of the State of Explainable AI for Natural Language Processing AACL 2020 Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability NeurIPS 2020 Concise Explanations of Neural Networks using Adversarial Training ICML 2020 Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling ACL 2020 Debugging Tests for Model Explanations NeurIPS 2020 DeepCoDA: personalized interpretability for compositional health data ICML 2020