Interpretability

PyDL8.5: a Library for Learning Optimal Decision Trees IJCAI 2020 QA2Explanation: Generating and Evaluating Explanations for Question Answering Systems over Knowledge Graph EMNLP 2020 Quantifying Attention Flow in Transformers ACL 2020 R4C: A Benchmark for Evaluating RC Systems to Get the Right Answer for the Right Reason ACL 2020 RANCC: Rationalizing Neural Networks via Concept Clustering COLING 2020 Rationalizing Medical Relation Prediction from Corpus-level Statistics ACL 2020 Reasoning on Knowledge Graphs with Debate Dynamics AAAI 2020 Re-Attention for Visual Question Answering AAAI 2020 Recalibrating classifiers for interpretable abusive content detection EMNLP 2020 Recurrent Dirichlet Belief Networks for interpretable Dynamic Relational Data Modelling IJCAI 2020 Red Dragon AI at TextGraphs 2020 Shared Task : LIT : LSTM-Interleaved Transformer for Multi-Hop Explanation Ranking COLING 2020 Reevaluating Adversarial Examples in Natural Language EMNLP 2020 Reference to Discourse Topics: Introducing “Global” Shell Nouns COLING 2020 Regional Tree Regularization for Interpretability in Deep Neural Networks AAAI 2020 Regularizing Black-box Models for Improved Interpretability NeurIPS 2020 Relation-Based Counterfactual Explanations for Bayesian Network Classifiers IJCAI 2020 Relation Extraction with Explanation ACL 2020 Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks AAAI 2020 Rescuing neural spike train models from bad MLE NeurIPS 2020 Restricting the Flow: Information Bottlenecks for Attribution ICLR 2020 Rethinking Self-Attention: Towards Interpretability in Neural Parsing EMNLP 2020 Reverse Engineering Configurations of Neural Text Generation Models ACL 2020 Reverse-engineering deep ReLU networks ICML 2020 Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice NeurIPS 2020 Robot Action Selection Learning via Layered Dimension Informed Program Synthesis CoRL 2020