Most papers that are submitted with physics inspired approaches are rejected from conferences. There is indeed a mono culture, but it's not due to a bias in physics. Maybe physics envy.
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Replying to @IntuitMachine
That's interesting... so the monoculture may be specific to deep learning as such; something that developed just within that narrow field. (
@michael_nielsen, maybe this is the point you were making too?)2 replies 0 retweets 2 likes -
Replying to @Meaningness @IntuitMachine
Pretty much. There are useful ideas from physics in deep learning research, but I don't think any (currently) really key/dominant ideas are from physics. Maybe I'm not thinking of one?
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Replying to @michael_nielsen @IntuitMachine
I mean... the whole thing looks like physics to me? You’re shaking a ball on an energy surface.
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Replying to @Meaningness @IntuitMachine
Minimizing a cost function using SGD/backprop is like physics in much the same way as eigenvectors are part of quantum mechanics.
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It occurs to me that I don't know where the theory of mathematical optimization (& notions like objective function, gradient descent) comes from. Certainly, it was buzzing as a subject independent of physics by the 1950s. But its roots may be in part in physics, earlier.
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Replying to @michael_nielsen @Meaningness
The divergence must be very long ago. Lagrange multiplier (i.e. regularization) is very old. Only the physicists know what a Lagrangian is! The concept of Hamiltonian or Lagrangian Dynamics is alien.
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Replying to @IntuitMachine @michael_nielsen
Ok, this is weird to me, because as I remember it the connection with Hamiltonian dynamics was super salient in the field when I was involved. That was 30 years ago and I guess everything is different.
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But, presumably the reason so many physicists go into DL now is that the type of math is so familiar to them?
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Replying to @Meaningness @michael_nielsen
Likely true. The math is familiar. But there's very little physics.
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Well I was suggesting “most of the math in DL comes from physics,” not “most of the math from physics is relevant to ML”
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