optics   3887

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Photography optics in the time dimension | Nature Photonics
New trick for fast imaging, can shrink a 30cm optics train to 3cm
optics  physics  fast  imaging  research  technology  photography 
2 days ago by asteroza
Novel optics for ultrafast cameras create new possibilities for imaging
MIT researchers have developed novel photography optics that capture images based on the timing of reflecting light inside the optics, instead of the traditional approach that relies on the arrangement of optical components. These new principles, the researchers say, open doors to new capabilities for time- or depth-sensitive cameras, which are not possible with conventional photography optics.
optics 
4 days ago by euler
Lessons from Optics, The Other Deep Learning – arg min blog
It would be easier to design deep nets if we could talk about the action of each of its layers the way we talk about the action of an optical element on the light that passes through it.

We talk about convolutional layers as running matched filters against their inputs, and the subsequent nonlinearities as pooling. This is a relatively low-level description, akin to describing the action of a lens in terms of Maxwell’s equations.

Maybe there are higher level abstractions to rely on, in terms of a quantity that is modified as it passes through the layers of a net, akin to the action of lens in terms of how it bends rays.

And it would be nice if this abstraction were quantitative so you could plug numbers into a formula to run back-of-the-envelope analyses to help you design your network.

It would be easier to design deep nets if we could talk about the action of each of its layers the way we talk about the action of an optical element on the light that passes through it.

We talk about convolutional layers as running matched filters against their inputs, and the subsequent nonlinearities as pooling. This is a relatively low-level description, akin to describing the action of a lens in terms of Maxwell’s equations.

Maybe there are higher level abstractions to rely on, in terms of a quantity that is modified as it passes through the layers of a net, akin to the action of lens in terms of how it bends rays.

And it would be nice if this abstraction were quantitative so you could plug numbers into a formula to run back-of-the-envelope analyses to help you design your network.

There’s a mass influx of newcomers to our field and we’re equipping them with little more than folklore and pre-trained deep nets, then asking them to innovate. We can barely agree on the phenomena that we should be explaining away. I think we’re far from teaching this stuff in high schools.
deeplearning  optics  physics  education 
20 days ago by mike

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