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pro vyhledávání: '"Widdicombe, Amy"'
Large language models (LLMs) are becoming bigger to boost performance. However, little is known about how explainability is affected by this trend. This work explores LIME explanations for DeBERTaV3 models of four different sizes on natural language
Externí odkaz:
http://arxiv.org/abs/2405.05348
Autor:
Widdicombe, Amy, Julier, Simon J.
Binarized Neural Networks (BNNs) have the potential to revolutionize the way that deep learning is carried out in edge computing platforms. However, the effectiveness of interpretability methods on these networks has not been assessed. In this paper,
Externí odkaz:
http://arxiv.org/abs/2106.12569
Publikováno v:
Published in the DAIS 2019 - Workshop on Distributed Analytics InfraStructure and Algorithms for Multi-Organization Federations
Deep Neural Networks (DNNs) deliver state-of-the-art performance in many image recognition and understanding applications. However, despite their outstanding performance, these models are black-boxes and it is hard to understand how they make their d
Externí odkaz:
http://arxiv.org/abs/1908.04389