论文标题
差分方程和概率灵感的图形神经网络用于潜在变量学习
Differential equation and probability inspired graph neural networks for latent variable learning
论文作者
论文摘要
概率理论和微分方程是机器学习模型设计的可解释性和指导的强大工具，尤其是从观察中阐明学习潜在变量的数学动机。子空间学习映射低维子空间上的高维特征，以捕获有效的表示。图表广泛用于建模潜在的可变学习问题，图形神经网络在图上实现了深度学习体系结构。受概率理论和微分方程的启发，本文进行了有关图形神经网络的注释和建议，以通过变异推断和微分方程来解决子空间学习问题。
Probabilistic theory and differential equation are powerful tools for the interpretability and guidance of the design of machine learning models, especially for illuminating the mathematical motivation of learning latent variable from observation. Subspace learning maps high-dimensional features on low-dimensional subspace to capture efficient representation. Graphs are widely applied for modeling latent variable learning problems, and graph neural networks implement deep learning architectures on graphs. Inspired by probabilistic theory and differential equations, this paper conducts notes and proposals about graph neural networks to solve subspace learning problems by variational inference and differential equation.
