<ul><li>Logic gate networks (LGNs) aim to provide efficient solutions for image classification by learning a network of logic gates.</li><li>Training LGNs to solve even simple problems like CIFAR-10 can take days to weeks, with almost half of the network remaining unused, leading to a discretization gap.</li><li>Researchers have introduced Gumbel noise with a straight-through estimator during training to speed up training, improve neuron utilization, and reduce the discretization gap in LGNs.</li><li>This approach results in training networks 4.5 times faster, reducing the discretization gap by 98%, and eliminating the number of unused gates by 100%.</li></ul>

Mind the Gap: Removing the Discretization Gap in Differentiable Logic Gate Networks

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