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

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21#
發(fā)表于 2025-3-25 04:06:24 | 只看該作者
22#
發(fā)表于 2025-3-25 07:54:49 | 只看該作者
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.
23#
發(fā)表于 2025-3-25 11:44:53 | 只看該作者
24#
發(fā)表于 2025-3-25 17:32:58 | 只看該作者
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
25#
發(fā)表于 2025-3-25 21:11:40 | 只看該作者
26#
發(fā)表于 2025-3-26 01:23:22 | 只看該作者
27#
發(fā)表于 2025-3-26 08:18:34 | 只看該作者
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.
28#
發(fā)表于 2025-3-26 09:53:41 | 只看該作者
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.
29#
發(fā)表于 2025-3-26 12:39:17 | 只看該作者
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.
30#
發(fā)表于 2025-3-26 19:38:15 | 只看該作者
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.
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