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James Wexler

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16

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3091

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Best of both worlds: local and global explanations with human-understandable concepts

2021 arXiv: Learning
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The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models

2020 arXiv: Computation and Language
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Probing ML models for fairness with the what-if tool and SHAP: hands-on tutorial.

2020
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The What-If Tool: Interactive Probing of Machine Learning Models

2020 IEEE Transactions on Visualization and Computer Graphics
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Automating Interpretability: Discovering and Testing Visual Concepts Learned by Neural Networks.

2019 arXiv: Machine Learning
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Towards Automatic Concept-based Explanations.

2019 arXiv: Machine Learning
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Approachable music composition with machine learning at scale

2019 ISMIR | International Symposium/Conference on Music Information Retrieval
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The What-If Tool: Interactive Probing of Machine Learning Models

2019 arXiv: Learning
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Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

2018 ICML | International Conference on Machine Learning
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Scalable and accurate deep learning with electronic health records

2018 arXiv: Computers and Society
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Citations (807)