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Contrasting deep learning with optics is an interesting idea; and one that should be taken far more literally. E.g. coherent optical processing can be used to perform feature extraction for image classificationpic.twitter.com/LCGEHbwitg
Intuitively, differentiable layered representation learning (deep learning) is in some ways closer to classical signal processing than it is to other branches of AI and ML, due to its substrate (stack of learned linear filters with a hardcoded differentiable non-linearity)
I wonder how differently the field would have evolved if we had chosen to use terminology derived from the world of signal processing instead of neuropsychology
Check out @LightOnIO, by @IgorCarron et al.
Indeed, we use coherent light for Machine Learning. And soon enough, people will be able to use our technology. We will have our next newsletter soon with more details: http://eepurl.com/cv_1h5
On a related note @alirahimi0 recently blogged on the things deep learning can adopt from optics.https://twitter.com/TheShubhanshu/status/956630915152785408 …
The emerging of deep learning in cavity quantum electrodynamics (?)
Can you backpropagate the light?
@alirahimi0 has an interesting take on this question of analogy with optics recently as well http://www.argmin.net/2018/01/25/optics/ …
'convolutional filter bank', just like 'wavelet filter bank'. A wonderful idea.
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