Yet another AI engineer. @ Computer Vision Lab, Seoul National University. Research: Generative AI & Representation Learningjaerinlee.com EarthJoined August 2015
some thoughts on the grokfast paper, which has some fun theory + nice results but i suspect is maybe fluffing up a simpler thing that would also work https://t.co/xL7v8JSY23
Oh, sorry for mistaking your question.
tl;dr: I am very open to other types of gradient filters that can have smooth transitions like MA/EMA or can have sharp cutoffs like traditional FIR filters. However, in my short experiences, I couldn't find a better sharp-transition…
Oh, sorry for mistaking your question.
tl;dr: I am very open to other types of gradient filters that can have smooth transitions like MA/EMA or can have sharp cutoffs like traditional FIR filters. However, in my short experiences, I couldn't find a better sharp-transition… https://t.co/uQh2Il0qBT
@garybasin I’m stunned at the fact they thought to try this
Modeling gradients as signals? Wtf? This seems like a bigger deal than their application, was there prior art?
Grokked weights are much closer to random init in weight space than where models are ending up today. Makes some sense intuitively but pretty cool to see.
Grokked weights are much closer to random init in weight space than where models are ending up today. Makes some sense intuitively but pretty cool to see.
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