explainable ai

Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations AAAI 2021 IST-Unbabel 2021 Submission for the Explainable Quality Estimation Shared Task EMNLP 2021 KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference ACL 2021 Learning to Explain: Generating Stable Explanations Fast ACL 2021 Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data ICML 2021 On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning AAAI 2021 On the Tractability of SHAP Explanations AAAI 2021 Quantifying Explainers of Graph Neural Networks in Computational Pathology CVPR 2021 RADAR-X: An Interactive Interface Pairing Contrastive Explanations with Revised Plan Suggestions AAAI 2021 Reasoning-Based Learning of Interpretable ML Models IJCAI 2021 Reinforcement Learning Enhanced Explainer for Graph Neural Networks NeurIPS 2021 Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting ICLR 2021 SCOUTER: Slot Attention-Based Classifier for Explainable Image Recognition ICCV 2021 “Something Something Hota Hai!” An Explainable Approach towards Sentiment Analysis on Indian Code-Mixed Data EMNLP 2021 SWAG: Superpixels Weighted by Average Gradients for Explanations of CNNs WACV 2021 The Utility of Explainable AI in Ad Hoc Human-Machine Teaming NeurIPS 2021 Transformer Interpretability Beyond Attention Visualization CVPR 2021 Trees with Attention for Set Prediction Tasks ICML 2021 What Makes a Scientific Paper be Accepted for Publication? EMNLP 2021 xER: An Explainable Model for Entity Resolution using an Efficient Solution for the Clique Partitioning Problem NAACL 2021 XProtoNet: Diagnosis in Chest Radiography With Global and Local Explanations CVPR 2021 AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models JMLR 2020 A New Perspective on Learning Context-Specific Independence PGM 2020 CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines AAAI 2020 Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? ACL 2020