Comments (3)
Hi!,
I would need to read it carefully but my first impression is that they focus on natural language processing datasets. Using then a methodology really intrinsic to the type of data: lexicons....
In our case, we are dealing with any n-dimensional dataset, and, using a nearness condition, reduce the size of data, i.e., the number of points. Then, we propose as a tool to measure the similarity of two different datasets both Hausdorff distance and Bottleneck distance.
Besides, it seems that in the paper you provide, they are trying to discriminate if MLP can really generalize datasets of NLP or not.
Thank you for your interest in our paper. You can find it here.
Greetings
from experiments-representative-datasets.
great thanks
you explained very well
do you have this kind of clear explanation in written for example presentation slides: less scientific but more practicable?
from experiments-representative-datasets.
Not yet. However, if I do some slides, I would add them to this repository.
from experiments-representative-datasets.
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from experiments-representative-datasets.