4 ms·

UCLA researchers create all-optical diffractive deep neural network (2018)

• deleted 6y ago
  [deleted]
• colincooke 6y ago
  I'll start off by saying that this type of research is very interesting, and in this case well done. However, the issue to me in "all optical neural nets" has always been the non-linearity. The non-lineartiy (or activation function) is crucial to the high performance we see with deep neural networks. For example, on the task of interest here (MNIST, a classic hand-written digit classification task), a old-school linear classifier can get between 88% and 92.4% accuracy [1]. Whereas non-linear neural networks easily achieve 95%+ [1]. So until they figure out a way to represent a nice non-linearity (unless I missed something), these all optical techniques are at a permanant disadvantage. [1] http://yann.lecun.com/exdb/mnist/ http://yann.lecun.com/exdb/mnist/
  • fny 6y ago
    There has been a decent amount of research into optical ReLUs. So the nonlinearity you seek is possible.
• skybrian 6y ago
  From 2018. Looks like the paper has lots of citations: https://scholar.google.com/scholar?cites=6457098903910716621&as_sdt=2005&sciodt=0,5&hl=en&scioq=all-optical+deep+learning https://scholar.google.com/scholar?cites=6457098903910716621...
• uoaei 6y ago
  Here is another paper which shows that one can use PyTorch to create a simulation environment to train (learn) an inhomogenous medium to guide a solution of the wave equation such that the mapping between input and output closely fits a dataset, in this case classifying spoken vowels. https://advances.sciencemag.org/content/5/12/eaay6946 https://advances.sciencemag.org/content/5/12/eaay6946 And the repo: https://github.com/fancompute/wavetorch https://github.com/fancompute/wavetorch