Auditing Deep Learning processes through Kernel-based Explanatory Models (D19-1)
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| Challenge: | Existing nonlinearity of deep learning models can be a major drawback . ethical accountability of such systems is becoming a crucial issue . |
| Approach: | They propose to use Layerwise Relevance Propagation to trace back connections between linguistic properties of input instances and system decisions. |
| Outcome: | The proposed model evaluates the transparency and coherence of analogy-based explanations modeling an audit stage for the system. |
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| Challenge: | In this paper, we examine the behavior of deep learning models in their intermediate layers . saliency determines what is critical for the final decision of a deep model . |
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Infusing Finetuning with Semantic Dependencies (2021.tacl-1)
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| Challenge: | Several diagnostics help to localize the benefits of our approach. |
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| Challenge: | Existing work on deep neural networks has focused on representation analysis, but recent work focused on analyzing neurons within these models. |
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