Titlebook: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning; Wojciech Samek,Grégoire Montavon,Klaus-Robert Müll Book 2019 Sprin

ingenue

绝食

Explaining and Interpreting LSTMsque used for explaining the predictions of feed-forward networks to the LSTM architecture used for sequential data modeling and forecasting. The special accumulators and gated interactions present in the LSTM require both a new propagation scheme and an extension of the underlying theoretical framework to deliver faithful explanations.

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cringe

0302-9743 urse and provides directions of future development.The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up hu

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molest

不要严酷

Michel Tenenhaus,Mohamed Hanafique used for explaining the predictions of feed-forward networks to the LSTM architecture used for sequential data modeling and forecasting. The special accumulators and gated interactions present in the LSTM require both a new propagation scheme and an extension of the underlying theoretical framework to deliver faithful explanations.

帐单

Cancer-Related Pain in Childhood,t computation and one based on a propagation mechanism. We evaluate them using three “axiomatic” properties: ., ., and .. These properties are tested on the overall explanation, but also at intermediate layers, where our analysis brings further insights on how the explanation is being formed.

引起

Carol M. Trivette,Catherine P. Corrttribution methods and show how they share the same idea of using the gradient information as a descriptive factor for the functioning of a model. Finally, we discuss the strengths and limitations of these methods and compare them with available alternatives.

STYX

Gradient-Based Attribution Methodsttribution methods and show how they share the same idea of using the gradient information as a descriptive factor for the functioning of a model. Finally, we discuss the strengths and limitations of these methods and compare them with available alternatives.