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from the paper: "[...] learnable network parameter that is iteratively adjusted during the training process of the diffractive network, using an error back-pro
by flohrian 8y ago
from the paper:
"[...] learnable network parameter that is iteratively adjusted during the training process of the diffractive network, using an error back-propagation method. After this numerical training phase implemented in a computer, the D^2NN design is fixed and the transmission/reflection coefficients of the neurons of all the layers are determined. This D^2NN design, once physically fabricated using e.g., 3D-printing, 3lithography, etc., can then perform, at the speed of light propagation, the specific task that it is trained for, using only optical diffraction and passive optical components/layers, creating an efficient and fast way of implementing machine learning tasks."
- andrewjrangel 8y agoSo the optical component is only the end result model of the NN? It isn't learning using the optics?
- dqpb 8y agoWhich is unfortunate, since learning is the compute intensive part
- dekhn 8y agoOnly indirectly. the physical device only does feedforward so they had to train it using tensorflow on a conventional device.
- cheeko1234 8y agoCheck out this new article: https://www.osa.org/en-us/about_osa/newsroom/news_releases/2018/researchers_move_closer_to_completely_optical_arti/ https://www.osa.org/en-us/about_osa/newsroom/news_releases/2... They implemented a back propagation algorithm using just optical.