Phase transitions in the mini-batch size for sparse and dense two-layer neural networks
Raffaele Marino,
Federico Ricci-Tersenghi
Abstract:The use of mini-batches of data in training artificial neural networks is nowadays very common. Despite its broad usage, theories explaining quantitatively how large or small the optimal mini-batch size should be are missing. This work presents a systematic attempt at understanding the role of the mini-batch size in training two-layer neural networks.
Working in the teacher-student scenario, with a sparse teacher, and focusing on tasks of different complexity, we quantify the effects of changing the mi… Show more
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