Latent SHAP: Toward Practical Human-Interpretable Explanations

Latent SHAP: Toward Practical Human-Interpretable Explanations

27 November 2022

Satoru Momiyama

Toshinori Araki

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Papers citing "Latent SHAP: Toward Practical Human-Interpretable Explanations"

10 / 10 papers shown

Title
Human-interpretable model explainability on high-dimensional data Damien de Mijolla Christopher Frye M. Kunesch J. Mansir Ilya Feige FAtt 44 10 0 14 Oct 2020
Language Models are Few-Shot Learners Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan ... Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever Dario Amodei BDL 829 42,332 0 28 May 2020
Explaining Classifiers with Causal Concept Effect (CaCE) Yash Goyal Amir Feder Uri Shalit Been Kim CML 83 178 0 16 Jul 2019
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Jacob Devlin Ming-Wei Chang Kenton Lee Kristina Toutanova VLM SSL SSeg 1.8K 95,114 0 11 Oct 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 219 1,842 0 30 Nov 2017
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 117 2,360 0 01 Nov 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 22,002 0 22 May 2017
Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention Jinkyu Kim John F. Canny FAtt XAI OOD MILM CML 85 336 0 30 Mar 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,027 0 16 Feb 2016
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification Kaiming He Xinming Zhang Shaoqing Ren Jian Sun VLM 326 18,647 0 06 Feb 2015

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