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RelatIF: Identifying Explanatory Training Examples via ...
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由 E Barshan 著作2020被引用 114 次 — RelatIF considers the local influence that an explanatory example has on a prediction relative to its global effects on the model. In empirical ...
Identifying Explanatory Training Examples via Relative Influence
Proceedings of Machine Learning Research
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由 E Barshan 著作2020被引用 114 次 — RelatIF con- siders the local influence that an explanatory example has on a prediction relative to its global effects on the model. In empirical eval- uations, ...
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Identifying Explanatory Training Examples via Relative ...
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RelatIF is introduced, a new class of criteria for choosing relevant training examples by way of an optimization objective that places a constraint on global ...
RelatIF: Identifying Explanatory Training Samples via ...
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RelatIF: Identifying Explanatory Training Samples via Relative Influence ... explanatory example has on a prediction relative to its global effects on the model.
Identifying Explanatory Training Samples via Relative ...
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2020年8月26日 — RelatIF: Identifying Explanatory Training Samples via Relative Influence. Aug 26, 2020. Speakers. EB · Elnaz Barshan. Speaker · 0 followers.
arXiv:2401.15241v2 [cs.CL] 13 Jun 2024
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由 M Isonuma 著作2024被引用 4 次 — Relatif: Identifying explanatory training samples via relative influence. In International Conference on Artificial. Intelligence and ...
Elnaz Barshan | ServiceNow Research
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RelatIF: Identifying Explanatory Training Examples via Relative Influence. Elnaz Barshan, Marc-Etienne Brunet , Gintare Karolina Dziugaite.
How Many and Which Training Points Would Need to be ...
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由 J Yang 著作2023被引用 5 次 — Relatif: Identifying explanatory training samples via relative influence. In International Conference on Artificial. Intelligence and ...
14 頁
Training Data Influence Analysis & Estimation Resources
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RelatIF: Identifying Explanatory Training Samples via Relative Influence [link] [video]. Elnaz Barshan, Marc-Etienne Brunet, and Gintare Karolina Dziugaite ...
Marc-Etienne Brunet
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In this work, we focus on the use of influence functions to identify relevant training examples that one might hope "explain" the predictions of a machine ...