Interpretability

Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models ACL 2021 Contrastive Explanations for Model Interpretability EMNLP 2021 Counterfactual Explanations for Oblique Decision Trees:Exact, Efficient Algorithms AAAI 2021 Creating Interpretable Data-Driven Approaches for Remote Health Monitoring AAAI 2021 Debiasing Methods in Natural Language Understanding Make Bias More Accessible EMNLP 2021 Direct Exploitation of Attention Weights for Translation Quality Estimation EMNLP 2021 Discretized Integrated Gradients for Explaining Language Models EMNLP 2021 Do Language Models Know the Way to Rome? EMNLP 2021 Evaluation of Similarity-based Explanations ICLR 2021 Evidence Inference Networks for Interpretable Claim Verification AAAI 2021 Explainable Models with Consistent Interpretations AAAI 2021 Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach AAAI 2021 Explanation-based Data Augmentation for Image Classification NeurIPS 2021 Explicable Reward Design for Reinforcement Learning Agents NeurIPS 2021 Exploratory Model Analysis Using Data-Driven Neuron Representations EMNLP 2021 Fast Axiomatic Attribution for Neural Networks NeurIPS 2021 Generating Realistic Natural Language Counterfactuals EMNLP 2021 Guided Integrated Gradients: An Adaptive Path Method for Removing Noise CVPR 2021 Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model JMLR 2021 HyDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks AAAI 2021 Improving Deep Learning Interpretability by Saliency Guided Training NeurIPS 2021 Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity ICML 2021 Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making ACL 2021 Interpretable Social Anchors for Human Trajectory Forecasting in Crowds CVPR 2021 Interpreting Multivariate Shapley Interactions in DNNs AAAI 2021