论文标题
独立的神经ODES具有灵敏度分析
Standalone Neural ODEs with Sensitivity Analysis
论文作者
论文摘要
本文介绍了独立的神经颂歌（Snode），这是一个连续的深入神经模型，能够描述完整的深神经网络。这使用了一种新型的非线性结合梯度（NCG）下降优化方案，用于训练，可以在其中合并Sobolev梯度以提高模型权重的平滑度。我们还提出了神经敏感性问题的一般表述，并显示了它在NCG培训中的使用方式。灵敏度分析提供了整个网络中不确定性传播的可靠度量，可用于研究模型鲁棒性并产生对抗性攻击。我们的评估表明，与Resnet模型相比，我们的新型配方会提高鲁棒性和性能，并且为了提高了可解释性，它为设计和开发机器学习的新机会提供了新的机会。
This paper presents the Standalone Neural ODE (sNODE), a continuous-depth neural ODE model capable of describing a full deep neural network. This uses a novel nonlinear conjugate gradient (NCG) descent optimization scheme for training, where the Sobolev gradient can be incorporated to improve smoothness of model weights. We also present a general formulation of the neural sensitivity problem and show how it is used in the NCG training. The sensitivity analysis provides a reliable measure of uncertainty propagation throughout a network, and can be used to study model robustness and to generate adversarial attacks. Our evaluations demonstrate that our novel formulations lead to increased robustness and performance as compared to ResNet models, and that it opens up for new opportunities for designing and developing machine learning with improved explainability.
