Not All Explanations for Deep Learning Phenomena Are Equally Valuable

This academic paper argues that not all explanations for deep learning phenomena hold equal value, particularly those observed in "edge cases" like double descent, grokking, and the lottery ticket hypothesis. The authors contend that focusing on narrow, ad hoc explanations for isolated phenomena is often inefficient and lacks practical utility in real-world applications. Instead, they advocate for a more pragmatic and scientific approach, urging researchers to leverage these phenomena as test beds for refining broad, generalizable explanatory theories of deep learning principles. The paper also provides actionable recommendations for improving research practices to maximize the broader impact and utility derived from studying these intriguing observations.

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