  With continual miniaturization ever more applications of deep learning can be
found in embedded systems, where it is common to encounter data with natural
complex domain representation. To this end we extend Sparse Variational Dropout
to complex-valued neural networks and verify the proposed Bayesian technique by
conducting a large numerical study of the performance-compression trade-off of
C-valued networks on two tasks: image recognition on MNIST-like and CIFAR10
datasets and music transcription on MusicNet. We replicate the state-of-the-art
result by Trabelsi et al. [2018] on MusicNet with a complex-valued network
compressed by 50-100x at a small performance penalty.
