model explanation

Explanation Consistency Training: Facilitating Consistency-Based Semi-Supervised Learning with Interpretability AAAI 2021 Explanations for Occluded Images ICCV 2021 Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations. AISTATS 2021 Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation ICLR 2021 Learning to Explain: Generating Stable Explanations Fast ACL 2021 On Explainability of Graph Neural Networks via Subgraph Explorations ICML 2021 On Explaining Random Forests with SAT IJCAI 2021 Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing EMNLP 2021 Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models ACL 2021 Probabilistic Sufficient Explanations IJCAI 2021 Provably efficient, succinct, and precise explanations NeurIPS 2021 Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting ICLR 2021 Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions AAAI 2021 Scaling Symbolic Methods using Gradients for Neural Model Explanation ICLR 2021 Thermostat: A Large Collection of NLP Model Explanations and Analysis Tools EMNLP 2021 Towards Fair and Transparent Algorithmic Systems IJCAI 2021 Understanding Interlocking Dynamics of Cooperative Rationalization NeurIPS 2021 Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling ACL 2020 ERASER: A Benchmark to Evaluate Rationalized NLP Models ACL 2020 Evaluating and Aggregating Feature-based Model Explanations IJCAI 2020 Evaluating Attribution Methods using White-Box LSTMs EMNLP 2020 Interpretable and Differentially Private Predictions AAAI 2020 On Second-Order Group Influence Functions for Black-Box Predictions ICML 2020 PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks NeurIPS 2020 Problems with Shapley-value-based explanations as feature importance measures ICML 2020