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Interpretability

7318 directly classified papers

Papers per year

Papers

Argumentation Quality Assessment: Theory vs. Practice ACL 2017
A Unified Approach to Interpreting Model Predictions NIPS 2017
Linguistically Regularized LSTM for Sentiment Classification ACL 2017
Rotated Word Vector Representations and their Interpretability EMNLP 2017
Structural Embedding of Syntactic Trees for Machine Comprehension EMNLP 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles NIPS 2017
Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation EMNLP 2017
Coupling Distributed and Symbolic Execution for Natural Language Queries ICML 2017
Learning Important Features Through Propagating Activation Differences ICML 2017
Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study ICML 2017
Acceptability Semantics for Weighted Argumentation Frameworks IJCAI 2017
Disambiguate or not? — The Role of Prosody in Unambiguous and Potentially Ambiguous Anaphora Production in Strictly Mandarin Parallel Structures INTERSPEECH 2017
Reading Validation for Pronunciation Evaluation in the Digitala Project INTERSPEECH 2017
Acoustic Properties of Canonical and Non-Canonical Stress in French, Turkish, Armenian and Brazilian Portuguese INTERSPEECH 2017
QUINT: Interpretable Question Answering over Knowledge Bases EMNLP 2017
Erasing Pattern Languages Distinguishable by a Finite Number of Strings ALT 2017
One Key to Sign Them All Considered Vulnerable: Evaluation of DNSSEC in the Internet NSDI 2017
Representativeness-aware Aspect Analysis for Brand Monitoring in Social Media IJCAI 2017
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations NIPS 2017
Learning to Predict Charges for Criminal Cases with Legal Basis EMNLP 2017
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction NIPS 2017
Model Checking Multi-Agent Systems against LDLK Specifications IJCAI 2017
Omniscient Debugging for Cognitive Agent Programs IJCAI 2017
Preterm Birth Prediction: Stable Selection of Interpretable Rules from High Dimensional Data MLHC 2016
Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making NIPS 2016