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 ...
Uncertainty may cause financial loss due to underestimation or overestimation of grade (Li et al., 2008; Dominy & Edgar, 2012; McManus et al., 2021a). For example, a 15% discrepancy in grade estimation can result in significant loss or project failure because mining production consumes be- ...
To address these issues, this paper introduces a novel post-hoc Sparsity-awar Uncertainty Calibration (SAUC) framework, which calibrates uncertainty in both zero and non-zero values. To develop SAUC, we firstly modify the state-of-the-art deterministic spatiotemporal Graph Neural Networks (ST-...
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 ...
A survey of uncertainty in deep neural networks Artif. Intell. Rev. (2022), pp. 1513-1589 arXiv:2107.03342 10.48550/arXiv.2107.03342 Google Scholar [38] L.V. Jospin, W. Buntine, F. Boussaid, H. Laga, M. Bennamoun Hands-on Bayesian neural networks – a tutorial for deep learning user...
These research efforts seek to address some critical challenges in high-dimensional dynamical systems, including but not limited to dynamical system identification, reduced order surrogate modelling, error covariance specification and model error correction. A large number of developed techniques and ...
Bayesian neural networks (BNN) [1, 15, 24, 37, 44] are well-known for modeling un- certainties in neural networks. The key idea of BNN is to learn the distribution over network weights [1] or fea- tures [56] instead of outputing a single fixed value. ...
Neural network (NN) interatomic potentials provide fast prediction of potential energy surfaces, closely matching the accuracy of the electronic structure methods used to produce the training data. However, NN predictions are only reliable within well-le
A survey of physics-of-failure and hybrid PHM for light-emitting diodes is proposed. • An overview of accelerated degradation tests for enhancing data quality. • Issues with epistemic uncertainty for LED PHM are discussed in connection with model definition and data collection. • Fusion of...
Keim et al. [65] described the VA process as a graph consisting of four major components (dataset, hypothesis, visualization, and insight). These components are connected by functions that allow to transform and analyze given input datasets while creating new insights, as shown in Fig.1. In ...