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To be honest, I'd readily cheer any groups working on traditional machine learning advancements despite all the current hype for neural methods.

I'll second that. For all the attention DL/ANNs get... there's still a lot of legwork going on out there using linear models, basic trees, etc. IIRC this years kaggle survey ranked Logistic Regression as the #1 most used model by a long shot.

neural networks are stacked logistic regressions. a lot of the deep learning research benefits logistic regression

you say that as if it's empty hype. but it's not: deep learning works, and works much better by any reasonable metric than SVMs in most problems that require high to very high model capacity.

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