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Funding" page. If there is no support, "None" must be listed. This is a required field in the submission process. Abstracts deemed to be purely for

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Anchors: High Precision Model-Agnostic Explanations

the goal of local model-agnostic interpretability (Ribeiro, Singh, and Guestrin 2016a; 2016b; Strumbelj and Kononenko 2010) is to explain the behavior of f(x) to a user, where f(x) is the individual prediction for instance x. The assumption is that while the model is …

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2017 SKUs are platform agnostic, so customers can run the software on either Windows or Linux. Editions overview The SQL Server 2017 editions align with how customers are deploying applications and solutions: • Enterprise Edition is ideal for applications requiring mission critical in-memory performance, security and high availability

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“Why Should I Trust You?” Explaining the Predictions of ...

“Why Should I Trust You?” Explaining the Predictions of Any Classifier Marco Tulio Ribeiro University of Washington Seattle, WA 98105, USA marcotcr@cs.uw.edu

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Model-Agnostic Meta-Learning for Fast Adaptation of Deep ...

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks large improvements in the task loss. The primary contribution of this work is a simple model-and task-agnostic algorithm for meta-learning that trains a model’s parameters such that a small number of gradi-ent updates will lead to fast learning on a new task. We

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Explainable Artificial Intelligence (XAI)

Local Interpretable Model-agnostic Explanations (LIME) The black -box model’s complex decision function f (unknown to LIME) is represented by the blue/pink background. The bright bold red cross is the instance being explained. LIME samples instances, gets predictions using f, and weighs them by the proximity to the instance being

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