Re:Hopular: Modern Hopfield Networks for Tabular Data
by latenightcoding on Monday June 06, 2022 @10:21PM
(#31649158)
That "notable criticism" is terrible.
His whole argument is that because this algorithm does not outperform sklearn's HistGradientBoostingClassifier the paper is useless and a waste of his "very precious time"
Re:Hopular: Modern Hopfield Networks for Tabular Data
by lcrmorin on Monday June 20, 2022 @08:49AM
(#31809718)
The tweets / medium article are a bit incomplete. But they mean at least:
- Authors didn't make decent efforts to build a robust baseline. Worse it seems that they have purposefully built a bad baseline to make their solution look better.
- Authors didn't really disclose full performance. Statistical performance is one thing but time complexity is another. Hopular needs 10 mins on a 500 rows datasets. That's a NO NO for any serious application.
- Authors didn't provide an easy to use interface. You can't really claim SoTA on small tabular data with something that isn't testable by everyone.
Re:Hopular: Modern Hopfield Networks for Tabular Data
by cgreerrun on Monday June 06, 2022 @11:56PM
(#31649732)
I think it is notable because he's an expert in that area and would know how to assess it. I'm not a fan of the tone either though...
But the authors claim "Hopular surpasses Gradient Boosting (e.g. XGBoost), Random Forests, and SVMs on tabular data." So if a simple sklearn model works better, it's worth knowing it might not be all it's cracked up to be before deciding to spend hours understanding another paper.
But the authors claim "Hopular surpasses Gradient Boosting (e.g. XGBoost), Random Forests, and SVMs on tabular data." So if a simple sklearn model works better, it's worth knowing it might not be all it's cracked up to be before deciding to spend hours understanding another paper.
The fact that SVM was the #2 algo in these results, and that linear methods beat out all the other methods means there's something going awry.