全面深入学习 (Holistic Deep Learning)

There is much interest in deep learning to solve challenges that arise in applying neural network models in real-world environments. In particular, three areas have received considerable attention: adversarial robustness, parameter sparsity, and output stability. Despite numerous attempts on solving these problems independently, there is very little work addressing the challenges simultaneously. In this paper, we address this problem of constructing holistic deep learning models by proposing a novel formulation that solves these issues in combination. Real-world experiments on both tabular and MNIST dataset show that our formulation is able to simultaneously improve the accuracy, robustness, stability, and sparsity over traditional deep learning models among many others.

翻译：人们非常希望深思熟虑,解决在现实世界环境中应用神经网络模型过程中出现的挑战。特别是,三个领域受到相当重视:对抗性强、参数宽度和产出稳定性。尽管为独立解决这些问题作出了许多努力,但同时应对这些挑战的工作却很少。在本文件中,我们通过提出一种新颖的提法来解决这些问题,来解决构建整体深度学习模型的问题。关于表格和MNIST数据集的现实世界实验表明,我们的提法能够同时改善传统的深层学习模型的准确性、稳健性、稳定性和广度。