A common problem when training neural networks is the size of the data1. There are several strategies for storing and querying large amounts of data, or for increasing model throughput to speed up training when there are large amounts of data, but scale causes problems in much more mundane …
In Super-Convergence: Very fast training of neural networks using large learning rates1, Smith and Tobin present evidence for a learning rate parametrization scheme that can result in a 10x decrease in training time, while maintaining similar accuracy. Specifically, they propose the use of a cyclical learning rate, which starts …
If you're getting started with PyTorch for text, you've probably encountered an error that looks something like:
Sizes of tensors must match except in dimension 0.
The short explanation for this error is that sequences are often different lengths, but tensors are required to be rectangular. The fix for this …
It is universally acknowledged that artificially augmented datasets lead to models which are both more accurate and more generalizable. They do this by introducing variability which is likely to be encountered in ecologically valid settings but is not present in the training data; and, by providing negative examples of spurious …
I recently needed to update some servers running an old Ubuntu LTS (Xenial, 16.04) to a slightly less old Ubuntu LTS (Bionic, 18.04). I had been putting it off for some time, mostly due to the noise I heard about problems installing the Nvidia CUDA toolkit. But that …
To help models generalize, it's common to use some form of data augmentation. This is where the original training data are modified in some way that preserves their semantics while changing their values. The model is fitted to the original training data, plus one or more augmented versions of it …