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Interpretability

173 directly classified papers

Papers per year

Papers

Attack to Explain Deep Representation CVPR 2020
MRI Reconstruction with Interpretable Pixel-Wise Operations Using Reinforcement Learning AAAI 2020
A Tale of a Probe and a Parser ACL 2020
Incorporating Priors with Feature Attribution on Text Classification ACL 2019
Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks AAAI 2019
Guiding the Flowing of Semantics: Interpretable Video Captioning via POS Tag EMNLP 2019
Many Faces of Feature Importance: Comparing Built-in and Post-hoc Feature Importance in Text Classification EMNLP 2019
Attention is not not Explanation EMNLP 2019
Learning Interpretable Negation Rules via Weak Supervision at Document Level: A Reinforcement Learning Approach NAACL 2019
Addressing Failure Prediction by Learning Model Confidence NIPS 2019
On the Accuracy of Influence Functions for Measuring Group Effects NIPS 2019
Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals AAAI 2019
Semantically Equivalent Adversarial Rules for Debugging NLP models ACL 2018
SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories EMNLP 2018
Human-in-the-Loop Interpretability Prior NIPS 2018
The Linguistic Ideologies of Deep Abusive Language Classification EMNLP 2018
Teaching Categories to Human Learners With Visual Explanations CVPR 2018
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections NIPS 2018
ConStance: Modeling Annotation Contexts to Improve Stance Classification EMNLP 2017
Inverting Visual Representations With Convolutional Networks CVPR 2016
Monotonic Calibrated Interpolated Look-Up Tables JMLR 2016
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction NIPS 2015
Understanding variable importances in forests of randomized trees NIPS 2013