Interpretability

Neural Response Interpretation Through the Lens of Critical Pathways CVPR 2021 Relevance-CAM: Your Model Already Knows Where To Look CVPR 2021 Transformer Interpretability Beyond Attention Visualization CVPR 2021 Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning ACL 2021 A Diagnostic Study of Explainability Techniques for Text Classification EMNLP 2020 Analyzing Individual Neurons in Pre-trained Language Models EMNLP 2020 Benchmarking Deep Learning Interpretability in Time Series Predictions NeurIPS 2020 Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors CVPR 2020 Explaining Knowledge Distillation by Quantifying the Knowledge CVPR 2020 Inserting Information Bottlenecks for Attribution in Transformers EMNLP 2020 Overlearning Reveals Sensitive Attributes ICLR 2020 There and Back Again: Revisiting Backpropagation Saliency Methods CVPR 2020 Towards Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping (Student Abstract) AAAI 2020 Attention is not not Explanation EMNLP 2019 Classifier-Agnostic Saliency Map Extraction AAAI 2019 From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction NeurIPS 2019 Identifying and Controlling Important Neurons in Neural Machine Translation ICLR 2019 Interpreting Deep Models for Text Analysis via Optimization and Regularization Methods AAAI 2019 NeuroX: A Toolkit for Analyzing Individual Neurons in Neural Networks AAAI 2019 Revealing the Dark Secrets of BERT EMNLP 2019 Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules? EMNLP 2018 Interpretable Neural Architectures for Attributing an Ad’s Performance to its Writing Style EMNLP 2018 Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections NeurIPS 2018 Interpreting Word-Level Hidden State Behaviour of Character-Level LSTM Language Models EMNLP 2018 Learning Explanations from Language Data EMNLP 2018