| Debugging Tests for Model Explanations | 0 | 0.34 | 2020 |
| Concept Bottleneck Models | 0 | 0.34 | 2020 |
| On Completeness-aware Concept-Based Explanations in Deep Neural Networks | 0 | 0.34 | 2020 |
| An Evaluation of the Human-Interpretability of Explanation. | 1 | 0.35 | 2019 |
| Visualizing and Measuring the Geometry of BERT. | 0 | 0.34 | 2019 |
| Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making. | 5 | 0.46 | 2019 |
| Automating Interpretability: Discovering and Testing Visual Concepts Learned by Neural Networks. | 0 | 0.34 | 2019 |
| Towards Automatic Concept-based Explanations | 4 | 0.40 | 2019 |
| Do Neural Networks Show Gestalt Phenomena? An Exploration of the Law of Closure. | 0 | 0.34 | 2019 |
| A Benchmark for Interpretability Methods in Deep Neural Networks | 5 | 0.39 | 2019 |
| Visualizing and Measuring the Geometry of BERT | 0 | 0.34 | 2019 |
| Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV). | 23 | 0.68 | 2018 |
| How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation. | 9 | 0.43 | 2018 |
| Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. | 8 | 0.45 | 2018 |
| Evaluating Feature Importance Estimates. | 4 | 0.39 | 2018 |
| xGEMs: Generating Examplars to Explain Black-Box Models. | 1 | 0.35 | 2018 |
| Learning how to explain neural networks: PatternNet and PatternAttribution | 17 | 0.58 | 2018 |
| To Trust Or Not To Trust A Classifier. | 9 | 0.44 | 2018 |
| Human-in-the-Loop Interpretability Prior. | 2 | 0.36 | 2018 |
| Sanity Checks for Saliency Maps. | 18 | 0.66 | 2018 |
| SmoothGrad: removing noise by adding noise. | 51 | 1.41 | 2017 |
| The (Un)reliability of saliency methods. | 19 | 0.73 | 2017 |
| QSAnglyzer: Visual Analytics for Prismatic Analysis of Question Answering System Evaluations | 0 | 0.34 | 2017 |
| A Roadmap for a Rigorous Science of Interpretability. | 42 | 1.93 | 2017 |
| Examples are not enough, learn to criticize! Criticism for Interpretability. | 11 | 0.48 | 2016 |
| The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification. | 31 | 1.48 | 2015 |
| Inferring Team Task Plans from Human Meetings: A Generative Modeling Approach with Logic-Based Prior. | 4 | 0.40 | 2015 |
| Coherent Predictive Inference under Exchangeability with Imprecise Probabilities. | 4 | 0.61 | 2015 |
| Scalable And Interpretable Data Representation For High-Dimensional, Complex Data | 4 | 0.43 | 2015 |
| Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction | 18 | 0.96 | 2015 |
| Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior. | 1 | 0.36 | 2013 |
| Machine Learning for Meeting Analysis. | 0 | 0.34 | 2013 |
| Learning about meetings | 4 | 0.42 | 2013 |
| Quantitative estimation of the strength of agreements in goal-oriented meetings | 2 | 0.37 | 2013 |
| Human-Inspired Techniques for Human-Machine Team Planning. | 0 | 0.34 | 2012 |
| Multiple relative pose graphs for robust cooperative mapping | 56 | 2.53 | 2010 |
