The reason most (not all) methods don't add value (over baseline) when scaled is because they're "extra training data in disguise", so their benefit vanishes in the high data regimehttps://twitter.com/ilyasut/status/1106323934209662976 …
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"New" algorithms introduce hardcoded prior "structure" to the solution, which baseline is capable of learning on its own, given enough data. Therefore, on large data sets, "new" loses its advantage over baseline.
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