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[P] Learning Digital Circuits: A Journey Through Weight Invariant Self-Pruning Neural Networks

TLDR: – Learns weight agnostic topologies which mimic digital circuits using gradient descent. – Sparse nets with weights restricted to 0 or 1 can be learned by introducing Bernoulli initialization to BinaryConnect framework. – In binarized net, neuron + activation act like OR gates. Network purely composed with OR gates fails to solve the complex problem. BatchNorm acts like a NOT gate allowing the network to learn more complicated functions by forming NOR gates. – Learning topologies don’t necessarily need signed weights (Refer SuperMasks: https://arxiv.org/abs/1905.01067).

Paper: https://arxiv.org/abs/1909.00052

Code: https://github.com/AgrawalAmey/learning-digital-net

Colab: https://colab.research.google.com/github/AgrawalAmey/learning-digital-net/

![](https://pbs.twimg.com/media/EDuoUq7UEAA3QPI?format=jpg&name=large)

submitted by /u/agrawalamey
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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.