QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration. (arXiv:2204.01701v1 [cs.LG])

The significant success of Deep Neural Networks (DNNs) is highly promoted by
the multiple sophisticated DNN libraries. On the contrary, although some work
have proved that Quadratic Deep Neuron Networks (QDNNs) show better
non-linearity and learning capability than the first-order DNNs, their neuron
design suffers certain drawbacks from theoretical performance to practical
deployment. In this paper, we first proposed a new QDNN neuron architecture
design, and further developed QuadraLib, a QDNN library to provide architecture
optimization and design exploration for QDNNs. Extensive experiments show that
our design has good performance regarding prediction accuracy and computation
consumption on multiple learning tasks.

Source: https://arxiv.org/abs/2204.01701


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