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Interpretability

173 directly classified papers

Papers per year

Papers

Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning NIPS 2021
Explicable Reward Design for Reinforcement Learning Agents NIPS 2021
Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration NIPS 2021
The effectiveness of feature attribution methods and its correlation with automatic evaluation scores NIPS 2021
Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness? ACL 2020
Obtaining Faithful Interpretations from Compositional Neural Networks ACL 2020
Towards Interpretable Natural Language Understanding with Explanations as Latent Variables NIPS 2020
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses NIPS 2020
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks NIPS 2020
Decisions, Counterfactual Explanations and Strategic Behavior NIPS 2020
Discovering Symbolic Models from Deep Learning with Inductive Biases NIPS 2020
Interpreting Twitter User Geolocation ACL 2020
Understanding Attention for Text Classification ACL 2020
Towards Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping (Student Abstract) AAAI 2020
AI Trust in Business Processes: The Need for Process-Aware Explanations AAAI 2020
Word-Level Contextual Sentiment Analysis with Interpretability AAAI 2020
Interpretable and Differentially Private Predictions AAAI 2020
Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks AAAI 2020
Evaluating Attribution Methods using White-Box LSTMs EMNLP 2020
Structured Self-Attention Weights Encode Semantics in Sentiment Analysis EMNLP 2020
Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation EMNLP 2020
Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language? EMNLP 2020
Inserting Information Bottlenecks for Attribution in Transformers EMNLP 2020
Zero-Shot Rationalization by Multi-Task Transfer Learning from Question Answering EMNLP 2020
Gradient-based Analysis of NLP Models is Manipulable EMNLP 2020