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Interpretability

7318 directly classified papers

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Papers

Explaining Neural Networks Semantically and Quantitatively ICCV 2019
Universal Adversarial Triggers for Attacking and Analyzing NLP IJCNLP 2019
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning IJCNLP 2019
Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes AISTATS 2019
Interpretation of Neural Networks Is Fragile AAAI 2019
Inverse Abstraction of Neural Networks Using Symbolic Interpolation AAAI 2019
Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for Multi-Hop QA ACL 2019
Does it Make Sense? And Why? A Pilot Study for Sense Making and Explanation ACL 2019
Interpretable Question Answering on Knowledge Bases and Text ACL 2019
Towards Explainable NLP: A Generative Explanation Framework for Text Classification ACL 2019
Deep Neural Model Inspection and Comparison via Functional Neuron Pathways ACL 2019
Incorporating Priors with Feature Attribution on Text Classification ACL 2019
Parallax: Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae ACL 2019
Do Human Rationales Improve Machine Explanations? ACL 2019
An Analysis of Source-Side Grammatical Errors in NMT ACL 2019
Computing Argumentative Explanations in Bipolar Argumentation Frameworks AAAI 2019
mdfa: Multi-Differential Fairness Auditor for Black Box Classifiers IJCAI 2019
Statistical Guarantees for the Robustness of Bayesian Neural Networks IJCAI 2019
Classifier-Agnostic Saliency Map Extraction AAAI 2019
On the Importance of Delexicalization for Fact Verification IJCNLP 2019
Broad-Coverage Semantic Parsing as Transduction IJCNLP 2019
Open Sesame: Getting inside BERT’s Linguistic Knowledge ACL 2019
Guiding the Flowing of Semantics: Interpretable Video Captioning via POS Tag IJCNLP 2019
Exploring Computational User Models for Agent Policy Summarization IJCAI 2019
Pathological Evidence Exploration in Deep Retinal Image Diagnosis AAAI 2019