graph neural networksmolecular property predictionQSPRuncertainty analysisDeep learning and graph‐based models have gained popularity in various life science applications such as property modeling, achieving state‐of‐the‐art performance. However, the quantification of prediction uncertainty in these models ...
Our model combines the interpretability of the statistical Tweedie family with the predictive power of graph neural networks, excelling in predicting a comprehensive range of crash risks. The decoder employs a compound Tweedie model, handling the non-Gaussian distribution inherent in crash data, with ...
Graph Neural Networks (GNNs) have improved unsupervised community detection of clustered nodes due to their ability to encode the dual dimensionality of the connectivity and feature information spaces of graphs. Identifying the latent communities has many practical applications from social networks to genom...
几篇论文实现代码:《Uncertainty quantification over graph with conformalized graph neural networks》(NeurIPS 2023) GitHub: github.com/snap-stanford/conformalized-gnn [fig3] 《Label-efficient Segme...
These networks act like ensemble methods in that they reduce the prediction variance but only use twice the number of parameters present in a regular neural network.这些网络用一个分布来表示每个参数,该分布由从共享潜在概率分布中得出的可能值的均值和方差定义(Blundell 等人,2015 年)。变分推理可以使用...
Modeling uncertainty in deep neural networks Modeling aleatoric uncertainty comes down to having a model predict a distribution over outputs rather than a point estimate. One way to do this is to have the model predict the parameters of a distribution as a function of the input. From this per...
For instance, graph neural networks have been applied for the prediction of compound c ardiotoxicity43. For drug development, assessing potential risks and adverse effects associated with candidate compounds as early as possible is highly desirable. The addition of uncertainty quantification methods ...
Zuo R, Xu Y (2023) Graph deep learning model for mapping mineral prospectivity. Math Geosci 55(1):1–21 Article Google Scholar Zuo R, Carranza EJM, Cheng Q (2012) Fractal/multifractal modelling of geochemical exploration data. J Geochem Explor 122:1–3 Article CAS Google Scholar Zuo R...
Batzner, S. et al. E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. , 3564–3572 (2019). Jinnouchi, R., Lahnsteiner, J., Karsai, F., Kresse, G. & Bokdam, M. Phase transitions of hybrid perovskites simulated by machine-learning force fields...
The digital twin model of mine ventilation system (DTMVS) plays an important role in intelligent safety management. However, the uncertainty of the ventilation resistance coefficient, which is the core parameter of the model, makes it challenging to accurately construct a DTMVS. In this study, La...