Titlebook: Explainable and Interpretable Models in Computer Vision and Machine Learning; Hugo Jair Escalante,Sergio Escalera,Marcel‘van Ger Book 2018

HUMP

Injazz J. Chen,Kenneth A. Paetschiscriminative than descriptions produced by existing captioning methods. In this work, we emphasize the importance of producing an explanation for an observed action, which could be applied to a black-box decision agent, akin to what one human produces when asked to explain the actions of a second h

啜泣

Daniel R. Williams,Norman McIntyrerowd-sourced human evaluation indicates that our ensemble visual explanation is significantly qualitatively outperform each of the individual system’s visual explanation. Overall, our ensemble explanation is better 61. of the time when compared to any individual system’s explanation and is also suff

過(guò)剩

Ahu Yazici Ayyildiz,Erdogan Kocons actually influence the output. This produces more succinct visual explanations and more accurately exposes the network’s behavior. We demonstrate the effectiveness of our model on three datasets totaling 16 h of driving. We first show that training with attention does not degrade the performance

epidermis

Derek L. Milne,Robert P. Reiserf a data-driven job candidate assessment system, intended to be explainable towards non-technical hiring specialists. In connection to this, we also give an overview of more traditional job candidate assessment approaches, and discuss considerations for optimizing the acceptability of technology-sup

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