explanation method

Explaining Temporal Graph Neural Network via Quantum-Inspired Evolutionary Algorithm AAAI 2026 Multi-Level Explanations for Generative Language Models ACL 2025 An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records EMNLP 2024 Comparing the Decision-Making Mechanisms by Transformers and CNNs via Explanation Methods CVPR 2024 Dissenting Explanations: Leveraging Disagreement to Reduce Model Overreliance AAAI 2024 Explanation in the Era of Large Language Models NAACL 2024 Improving LLM Attributions with Randomized Path-Integration EMNLP 2024 On the Faithfulness of Vision Transformer Explanations CVPR 2024 Position: Do Not Explain Vision Models Without Context ICML 2024 RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation ICML 2024 Training for Stable Explanation for Free NeurIPS 2024 Are Data-Driven Explanations Robust Against Out-of-Distribution Data? CVPR 2023 Cardinality-Minimal Explanations for Monotonic Neural Networks IJCAI 2023 On the Relationship Between Explanation and Prediction: A Causal View ICML 2023 Trade-off Between Efficiency and Consistency for Removal-based Explanations NeurIPS 2023 A Simplified Benchmark for Ambiguous Explanations of Knowledge Graph Link Prediction Using Relational Graph Convolutional Networks (Student Abstract) AAAI 2022 Do Explanations Explain? Model Knows Best CVPR 2022 Evaluating Explanations of Relational Graph Convolutional Network Link Predictions on Knowledge Graphs AAAI 2022 Fooling Explanations in Text Classifiers ICLR 2022 NoiseGrad — Enhancing Explanations by Introducing Stochasticity to Model Weights AAAI 2022 Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods AISTATS 2022 Lifelong Explainer for Lifelong Learners EMNLP 2021 The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations NeurIPS 2021 Towards Benchmarking the Utility of Explanations for Model Debugging NAACL 2021 What does LIME really see in images? ICML 2021