Search Results for author:
Found 2 papers, 0 papers with code
Data and Parameter Scaling Laws for Neural Machine Translation
We observe that the development cross-entropy loss of supervised neural machine translation models scales like a power law with the amount of training data and the number of non-embedding parameters in the model.
When Are Neural Pruning Approximation Bounds Useful?
Approximation bounds for neural network pruning attempt to predict the trade-off between sparsity and fidelity while shrinking neural networks.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
