Additive Noise Annealing and Approximation Properties of Quantized Neural Networks

24 May 2019Matteo SpallanzaniLukas CavigelliGian Paolo LeonardiMarko BertognaLuca Benini

We present a theoretical and experimental investigation of the quantization problem for artificial neural networks. We provide a mathematical definition of quantized neural networks and analyze their approximation capabilities, showing in particular that any Lipschitz-continuous map defined on a hypercube can be uniformly approximated by a quantized neural network... (read more)

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