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ITT: Whether SVMs are still relevant in the deep learning era. Some junior researchers will say neural networks are all you need. Industry folks will talk about
by bitforger 6y ago
ITT: Whether SVMs are still relevant in the deep learning era. Some junior researchers will say neural networks are all you need. Industry folks will talk about how they still use decision trees.
Personally, I'm quite bullish on the resurgence of SVMs as SOTA. What did it for me was Mikhail Belkin's talk at IAS.[1]
[1] https://m.youtube.com/watch?index=15&list=PLdDZb3TwJPZ5dqqg_S-rgJqSFeH4DQqFQ&v=5-Kqb80h9rk https://m.youtube.com/watch?index=15&list=PLdDZb3TwJPZ5dqqg_...
- rangerranvir 6y agoThanks for sharing. You actually shared a full playlist. Won't be able to get up before finishing a few of them.
- stu2b50 6y agoI mean NNs are still quite bad at low n tabular data (and they may always be), which is honestly how a lot of real life data is, so there is clearly a need for not a neural network. I feel like I've seem more tree ensembles in the wild than SVMs, though.
- rangerranvir 6y agoAnyway the idea of NNs was introduced to work on data which a simple human brain couldn't make sense of. For more general tabular data, like trees, regression and even rule based models are more realistic.
- ma2rten 6y agoI don't have time watch the video could you summarize why you think SVMs will become SOTA and on which problem?