Standalone Neural ODEs with Sensitivity Analysis

• URL: http://arxiv.org/abs/2205.13933v1
• Date: Fri, 27 May 2022 12:16:53 GMT
• Title: Standalone Neural ODEs with Sensitivity Analysis
• Authors: Rym Jaroudi, Luk\'a\v{s} Mal\'y, Gabriel Eilertsen, Tomas B. Johansson, Jonas Unger, George Baravdish
• Abstract summary: This paper presents a continuous-depth neural ODE model capable of describing a full deep neural network. We present a general formulation of the neural sensitivity problem and show how it is used in the NCG training. Our evaluations demonstrate that our novel formulations lead to increased robustness and performance as compared to ResNet models.
• Score: 5.565364597145569
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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