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[P] Wave Physics as an Analog Recurrent Neural Network

We just posted our new paper where we show that recurrent neural networks map to the physics of waves, used extensively to model optical, acoustic, and fluidic systems.

This is interesting because it enables one to build analog RNNs out of continuous wave-based physical systems, where the processing is performed passively through the propagation of waves through a domain.

These ‘wave RNNs’ are trained by backpropagation through the numerical wave simulation, which lets us optimize the pattern of material within their domain for a given ML task.

We demonstrate that this system can classify vowels through the injection of raw audio input to the domain.

Our paper can be found here: https://arxiv.org/abs/1904.12831

Our code for simulating and training the wave systems is built using pytorch and can be found here: https://github.com/fancompute/wavetorch

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.