Interpretability

Explaining Explanations: Axiomatic Feature Interactions for Deep Networks JMLR 2021 Explaining Hyperparameter Optimization via Partial Dependence Plots NeurIPS 2021 Explaining Local, Global, and Higher-Order Interactions in Deep Learning ICCV 2021 Explaining Neural Matrix Factorization with Gradient Rollback AAAI 2021 Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks NAACL 2021 Explaining Time Series Predictions with Dynamic Masks ICML 2021 Explanation Consistency Training: Facilitating Consistency-Based Semi-Supervised Learning with Interpretability AAAI 2021 Explanation in Constraint Satisfaction: A Survey IJCAI 2021 Explanations for Monotonic Classifiers. ICML 2021 Explanations for Occluded Images ICCV 2021 Explicable Reward Design for Reinforcement Learning Agents NeurIPS 2021 Explicit Knowledge Incorporation for Visual Reasoning CVPR 2021 Exploiting Definitions for Frame Identification EACL 2021 Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution ACL 2021 Exploiting Explanations for Model Inversion Attacks ICCV 2021 Exploiting Sparsity for Neural Network Verification L4DC 2021 Exploratory Model Analysis Using Data-Driven Neuron Representations EMNLP 2021 Exploring Distantly-Labeled Rationales in Neural Network Models ACL 2021 Exploring Explainable Selection to Control Abstractive Summarization AAAI 2021 Exploring Neural Language Models via Analysis of Local and Global Self-Attention Spaces EACL 2021 Exploring the Limits of Out-of-Distribution Detection NeurIPS 2021 Exploring the Role of BERT Token Representations to Explain Sentence Probing Results EMNLP 2021 Exploring Transitivity in Neural NLI Models through Veridicality EACL 2021 Fabula Entropy Indexing: Objective Measures of Story Coherence NAACL 2021 Factual Probing Is [MASK]: Learning vs. Learning to Recall NAACL 2021