Phase transitions in the mini-batch size for sparse and dense two-layer neural networks
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 m...
محفوظ في:
| المؤلفون الرئيسيون: | , |
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| التنسيق: | Artigo |
| اللغة: | Inglês |
| منشور في: |
IOP Publishing
2024-01-01
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| سلاسل: | Machine Learning: Science and Technology |
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://doi.org/10.1088/2632-2153/ad1de6 |
| الوسوم: |
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