Join our meetup, learn, connect, share, and get to know your Toronto AI community.
Browse through the latest deep learning, ai, machine learning postings from Indeed for the GTA.
Are you looking to sponsor space, be a speaker, or volunteer, feel free to give us a shout.
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
submitted by /u/BarnyardPuer
[link] [comments]