Challenges and Pitfalls of Machine Learning Evaluation and Benchmarking

29 Apr 2019Cheng LiAbdul DakkakJinjun XiongWen-mei Hwu

An increasingly complex and diverse collection of Machine Learning (ML) models as well as hardware/software stacks, collectively referred to as "ML artifacts", are being proposed - leading to a diverse landscape of ML. These ML innovations proposed have outpaced researchers' ability to analyze, study and adapt them... (read more)

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