142 citations · 162 across the 2 of their papers we have counts for
2 papers
cs.LG2021★ 142 cited
Explaining Neural Scaling Laws
Yasaman Bahri, Ethan Dyer, Jared Kaplan +2
The population loss of trained deep neural networks often follows precise power-law scaling relations with either the size of the training dataset or the number of parameters in th…
cs.LG2020★ 20 cited
A Neural Scaling Law from the Dimension of the Data Manifold
Utkarsh Sharma, Jared Kaplan
When data is plentiful, the loss achieved by well-trained neural networks scales as a power-law in the number of network parameters . This empirical scaling l…