
All-optical machine learning using diffractive deep neural networks (2018) - bra-ket
https://science.sciencemag.org/content/361/6406/1004
======
AndrewGYork
I asked this on Twitter, but maybe folks here can answer better: how important
is nonlinearity for deep neural networks? This method's output seems to be a
linear function of its (complex) input. Does that put important bounds on
performance?
[https://mobile.twitter.com/AndrewGYork/status/10228414045888...](https://mobile.twitter.com/AndrewGYork/status/1022841404588883968)
[https://news.ycombinator.com/item?id=17698135](https://news.ycombinator.com/item?id=17698135)

~~~
londons_explore
Simple examples can be constructed showing that nonlinearity is required for
certain problems.

There do exist non-linear optical components, so I assume that could be used
for a piece of followup work...

~~~
MrEldritch
True, but all the nonlinear optical effects I'm aware of only really start to
matter at very high intensities - so wouldn't really be applicable to the
kinds of scenarios they envision, like directly feeding it images seen from
ambient light.

~~~
AstralStorm
Uhm, speed of light differences in a modified crystal lattice are constant
nonlinearities reasonable to produce. They do not need high intensity light,
but they would need additional circuitry for scaling. Plus the network would
have to work on phase angle and not magnitude. Mostly Kerr effect (high
voltage) and cross wave polarization (e.g. given Pockel's cell) are useful
there.

------
nabla9
(2018)

This is only for inferencing. It can't learn.

