Interpretability

Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering COLING 2020 Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions ACL 2020 Explaining Groups of Points in Low-Dimensional Representations ICML 2020 Explaining the Explainer: A First Theoretical Analysis of LIME AISTATS 2020 Explaining Word Embeddings via Disentangled Representation AACL 2020 Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution ICLR 2020 Explanation by Progressive Exaggeration ICLR 2020 Explanation Perspectives from the Cognitive Sciences---A Survey IJCAI 2020 Explanation Regeneration via Multi-Hop ILP Inference over Knowledge Base COLING 2020 Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning ICLR 2020 Exploring End-to-End Differentiable Natural Logic Modeling COLING 2020 Exploring Interpretability in Event Extraction: Multitask Learning of a Neural Event Classifier and an Explanation Decoder ACL 2020 Exploring Weaknesses of VQA Models through Attribution Driven Insights ACL 2020 Extracting Headless MWEs from Dependency Parse Trees: Parsing, Tagging, and Joint Modeling Approaches ACL 2020 F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering EMNLP 2020 Fairwashing explanations with off-manifold detergent ICML 2020 Feature Importance Ranking for Deep Learning NeurIPS 2020 Feature relevance quantification in explainable AI: A causal problem AISTATS 2020 Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets COLING 2020 FIND: Human-in-the-Loop Debugging Deep Text Classifiers EMNLP 2020 Flexible distribution-free conditional predictive bands using density estimators AISTATS 2020 Formalizing Group and Propagated Trust in Multi-Agent Systems IJCAI 2020 Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics NeurIPS 2020 Further Analysis of Outlier Detection with Deep Generative Models NeurIPS 2020 GAT: Generative Adversarial Training for Adversarial Example Detection and Robust Classification ICLR 2020