Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures

Deep Unfolding: Build smarter, smaller neural nets for speech and sound

Think of a tried-and-true math idea, and a fast brain-like network, now put them together, they work hand-in-hand.
One starts with a model that knows rules about the problem, the other is a plain fast net that learns from examples.
The trick here is to turn each step of the old model's solution into a neural network layers, and then let each layer learn slightly different rules.
That gives a net that solves problems quickly, but keeps useful knowledge from the model, it feels both smart and simple.
You can train this compact net to clean up noisy voice or sounds, and it often matches bigger nets while using way fewer parameters.
The result is a practical method to make systems that are easier to run on phones, or in small devices, yet still do a great job.
This idea is simple to explain, surprising to see work, and could change how we build many real-world apps.

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Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures

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