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Interpretability for when NOT to use machine learning by Been Kim
Been Kim
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Been Kim (Google)
More from the Same Authors
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2021 : Interpretability of Machine Learning in Computer Systems: Analyzing a Caching Model »
Leon Sixt · Evan Liu · Marie Pellat · James Wexler · Milad Hashemi · Been Kim · Martin Maas -
2020 Poster: Debugging Tests for Model Explanations »
Julius Adebayo · Michael Muelly · Ilaria Liccardi · Been Kim -
2020 Poster: On Completeness-aware Concept-Based Explanations in Deep Neural Networks »
Chih-Kuan Yeh · Been Kim · Sercan Arik · Chun-Liang Li · Tomas Pfister · Pradeep Ravikumar -
2019 Poster: Towards Automatic Concept-based Explanations »
Amirata Ghorbani · James Wexler · James Zou · Been Kim -
2019 Poster: Visualizing and Measuring the Geometry of BERT »
Emily Reif · Ann Yuan · Martin Wattenberg · Fernanda Viegas · Andy Coenen · Adam Pearce · Been Kim -
2019 Poster: A Benchmark for Interpretability Methods in Deep Neural Networks »
Sara Hooker · Dumitru Erhan · Pieter-Jan Kindermans · Been Kim -
2018 Poster: Human-in-the-Loop Interpretability Prior »
Isaac Lage · Andrew Ross · Samuel J Gershman · Been Kim · Finale Doshi-Velez -
2018 Spotlight: Human-in-the-Loop Interpretability Prior »
Isaac Lage · Andrew Ross · Samuel J Gershman · Been Kim · Finale Doshi-Velez -
2018 Poster: Sanity Checks for Saliency Maps »
Julius Adebayo · Justin Gilmer · Michael Muelly · Ian Goodfellow · Moritz Hardt · Been Kim -
2018 Spotlight: Sanity Checks for Saliency Maps »
Julius Adebayo · Justin Gilmer · Michael Muelly · Ian Goodfellow · Moritz Hardt · Been Kim -
2018 Poster: To Trust Or Not To Trust A Classifier »
Heinrich Jiang · Been Kim · Melody Guan · Maya Gupta