期刊
出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3341216.3342210
关键词
explainable machine learning; post-hoc explanations; feature analysis; neural adaptive video streaming
Machine learning (ML) solutions to challenging networking problems, while promising, are hard to interpret; the uncertainty about how they would behave in untested scenarios has hindered adoption. Using a case study of an ML-based video rate adaptation model, we show that carefully applying interpretability tools and systematically exploring the model inputs can identify unwanted or anomalous behaviors of the model; hinting at a potential path towards increasing trust in ML-based solutions.
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