feature attribution

CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions NeurIPS 2021 Contrastive Explanations for Model Interpretability EMNLP 2021 DANCE: Enhancing saliency maps using decoys ICML 2021 Discretized Integrated Gradients for Explaining Language Models EMNLP 2021 Ecco: An Open Source Library for the Explainability of Transformer Language Models ACL 2021 Efficient Explanations from Empirical Explainers EMNLP 2021 Enhanced Regularizers for Attributional Robustness AAAI 2021 Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations EMNLP 2021 Enjoy the Salience: Towards Better Transformer-based Faithful Explanations with Word Salience EMNLP 2021 Error Causal inference for Multi-Fusion models NAACL 2021 Evaluating Saliency Methods for Neural Language Models NAACL 2021 Explaining by Removing: A Unified Framework for Model Explanation JMLR 2021 Explaining Deep Learning Models for Speech Enhancement Interspeech 2021 Explaining Explanations: Axiomatic Feature Interactions for Deep Networks JMLR 2021 Explaining Latent Representations with a Corpus of Examples NeurIPS 2021 Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks NAACL 2021 Explaining the Efficacy of Counterfactually Augmented Data ICLR 2021 Fast Axiomatic Attribution for Neural Networks NeurIPS 2021 Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information NeurIPS 2021 Flow-based Attribution in Graphical Models: A Recursive Shapley Approach ICML 2021 Foundations of Symbolic Languages for Model Interpretability NeurIPS 2021 Guided Integrated Gradients: An Adaptive Path Method for Removing Noise CVPR 2021 Improving Deep Learning Interpretability by Saliency Guided Training NeurIPS 2021 Interpreting Multivariate Shapley Interactions in DNNs AAAI 2021 Interpreting Text Classifiers by Learning Context-sensitive Influence of Words NAACL 2021