Interpretability

On the Realization of Compositionality in Neural Networks ACL 2019 Robust Attribution Regularization NeurIPS 2019 Towards Better Interpretability in Deep Q-Networks AAAI 2019 Verification of RNN-Based Neural Agent-Environment Systems AAAI 2019 Visualizing and Measuring the Geometry of BERT NeurIPS 2019 An Analysis of Encoder Representations in Transformer-Based Machine Translation EMNLP 2018 Boolean Decision Rules via Column Generation NeurIPS 2018 Human-in-the-Loop Interpretability Prior NeurIPS 2018 Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models NeurIPS 2018 Interpretable Convolutional Neural Networks CVPR 2018 Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections NeurIPS 2018 Model Agnostic Supervised Local Explanations NeurIPS 2018 Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations NeurIPS 2018 Net2Vec: Quantifying and Explaining How Concepts Are Encoded by Filters in Deep Neural Networks CVPR 2018 Portable, layer-wise task performance monitoring for NLP models EMNLP 2018 Rule induction for global explanation of trained models EMNLP 2018 SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories EMNLP 2018 Semantically Equivalent Adversarial Rules for Debugging NLP models ACL 2018 Teaching Categories to Human Learners With Visual Explanations CVPR 2018 The Linguistic Ideologies of Deep Abusive Language Classification EMNLP 2018 Towards better understanding of gradient-based attribution methods for Deep Neural Networks ICLR 2018 What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties ACL 2018 A Unified Approach to Interpreting Model Predictions NeurIPS 2017 ConStance: Modeling Annotation Contexts to Improve Stance Classification EMNLP 2017 Improving Interpretability of Deep Neural Networks With Semantic Information CVPR 2017