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.
Felix Petersen (Stanford University)
Christian Borgelt (Paris-Lodron-University of Salzburg)
Hilde Kuehne (Goethe University Frankfurt, MIT-IBM Waston AI Lab)
Prof. Dr. Hilde Kuehne is Head of Computer Vision and Machine Learning at the Computational Vision & Artificial Intelligence Group at the Goethe University Frankfurt and an affiliated professor at the MIT-IBM Watson AI Lab. Her research focuses on weakly and unsupervised recognition and understanding of video data. She obtained her doctoral degree in engineering from the Karlsruhe Institute of Technology (KIT) in 2014. Her experience includes projects with various European and US universities and international technology companies with a focus on image and video understanding processing. She has published various high-impact publications in the field, including the HMDB action classification dataset. She has organized various workshops in the field and served as area chair for CVPR, ICCV, and WACV. Beyond her work, she is committed to bringing more diversity to STEM.
Oliver Deussen (University of Konstanz)
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