3 ms·
TFA thinks that logarithmic x-axes are misleading. That's just not true here. I looked at the preprint and it's the right way to display data over such a large
by croemer 2y ago
TFA thinks that logarithmic x-axes are misleading. That's just not true here. I looked at the preprint and it's the right way to display data over such a large range.
The whole point is that below a threshold, you can increase training by a factor of 10 or 100 and there's barely any change. But at the threshold, the same relative increase suddenly produces big improvements.
- yorwba 2y ago> you can increase training by a factor of 10 or 100 and there's barely any change. How do you know accuracy didn't increase by a factor of 100 from 0.001% to 0.1%? Another factor of 100 for both and you're at 10% with "only" 10⁴ as many training FLOPS! If you want to use a nonlinear transformation to show very small and very large inputs in the same graph, surely a similar effort should be made to make changes in accuracy near the limits of 0% and 100% more visible.
- croemer 2y agoYou are right that using double log axes could be better, but the author doesn't ask for y-axis to be log-scaled but for x-axis to be linear.