论文标题
深层复杂值网络的贝叶斯稀疏方法
Bayesian Sparsification Methods for Deep Complex-valued Networks
论文作者
论文摘要
通过连续的微型化，可以在嵌入式系统中找到更多深度学习的应用，在这种系统中，遇到具有天然复杂域表示的数据是常见的。为此，我们将稀疏的变化液位扩展到复杂值的神经网络，并通过对C值网络的性能压缩权衡进行大规模数字研究来验证拟议的贝叶斯技术，以实现两项任务：MNIST类似MNIST的图像识别和CIFAR10数据集和Musicnet上的音乐转录。我们复制了Trabelsi等人的最新结果。 [2018]在Musicnet上，一个复杂值的网络以50-100倍的压缩，遭受了较小的性能罚款。
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.
