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pro vyhledávání: '"Katakkar, Anurag"'
Counterfactual explanations have been a popular method of post-hoc explainability for a variety of settings in Machine Learning. Such methods focus on explaining classifiers by generating new data points that are similar to a given reference, while r
Externí odkaz:
http://arxiv.org/abs/2410.14522
Autor:
Katakkar, Anurag, Black, Alan W
Language models (LMs) for text data have been studied extensively for their usefulness in language generation and other downstream tasks. However, language modelling purely in the speech domain is still a relatively unexplored topic, with traditional
Externí odkaz:
http://arxiv.org/abs/2111.00610
In attempts to develop sample-efficient and interpretable algorithms, researcher have explored myriad mechanisms for collecting and exploiting feature feedback (or rationales) auxiliary annotations provided for training (but not test) instances that
Externí odkaz:
http://arxiv.org/abs/2110.07566