tivotox t1_iuyb2oh wrote
Reply to comment by suflaj in What to tell about a model you make deeper and deeper, doesn't make better results but doesn't overfit as well? by [deleted]
The split has been done such as the train and test are highly different. the loss are almost equal on both datasets.
suflaj t1_iuybshu wrote
That seems very bad. You want your train-dev-test to be different samples of the same distribution, so, not very different sets.
Furthermore, if you're using test for model validation, that means you will have no dataset to finally evaluate your model on. Reconsider your process.
Finally, again, I urge you to evaluate your dataset on an established evaluation metric for the task, not the loss you use to train the model. What is the exact task?
[deleted] OP t1_iuyf897 wrote
[deleted]
suflaj t1_iuyg2am wrote
Well I couldn't understand what your task was when you didn't say what it was until now.
Other than that, skimming through the paper it quite clearly says the following:
> Our present results do not indicate our procedure can generalize to motifs that are not present in the training set
Because what they're doing doesn't generalize, I think the starting assumptions (that there will be imprevements with a larger model) are wrong, and so the question is unnecessary... The issue is with the method or the data, they do not elaborate more than that.
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