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Matrix and tensor decompositions for training binary neural networks
[article]
2019
arXiv
pre-print
This paper is on improving the training of binary neural networks in which both activations and weights are binary. While prior methods for neural network binarization binarize each filter independently, we propose to instead parametrize the weight tensor of each layer using matrix or tensor decomposition. The binarization process is then performed using this latent parametrization, via a quantization function (e.g. sign function) applied to the reconstructed weights. A key feature of our
arXiv:1904.07852v1
fatcat:z37niixs7rc3bhk35ljcteec2a