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Deep Differentiable Logic Gate Networks
Felix Petersen · Christian Borgelt · Hilde Kuehne · Oliver Deussen

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #431

Recently, research has increasingly focused on developing efficient neural network architectures. In this work, we explore logic gate networks for machine learning tasks by learning combinations of logic gates. These networks comprise logic gates such as "AND" and "XOR", which allow for very fast execution. The difficulty in learning logic gate networks is that they are conventionally non-differentiable and therefore do not allow training with gradient descent. Thus, to allow for effective training, we propose differentiable logic gate networks, an architecture that combines real-valued logics and a continuously parameterized relaxation of the network. The resulting discretized logic gate networks achieve fast inference speeds, e.g., beyond a million images of MNIST per second on a single CPU core.

Author Information

Felix Petersen (Stanford University)
Christian Borgelt (Paris-Lodron-University of Salzburg)
Hilde Kuehne (Goethe University Frankfurt)
Oliver Deussen (University of Konstanz)

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