Scalable Gaussian Process Regression Using Deep Neural Networks

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Neural Processes (2018, methodology): “Methods like (Calandra et al., 2016; Huang et al., 2015) remain fairly close to the GPs, but incor-Neural porate NNs to pre-process the input data.”
Uncertainty Quantification Using Neural Networks for Molecular Property Prediction (2020, methodology): “Signiﬁcant work has been done in UQ for NNs in an attempt to resolve this problem, both within and outside of the context of QSAR 3–15 including several comparative reviews.”
Optimizing connection weights in neural networks using the whale optimization algorithm (2016, methodology): “In the last three decades, ANNs have been widely investigated and applied to classiﬁcation, pattern recognition, regression, and forecasting problems (Schmid-huber 2015; Chatterjee et al 2016; Braik et al 2008; Linggard et al 2012; Huang et al 2015; Rezaeianzadeh et al. 2014).”
Drug discovery with explainable artificial intelligence (2020, methodology): “Union-based methods 163 ﬁrst train a neural network model and then feed its embeddings to a second model that handles uncertainty, such as a Gaussian process or a random forest.”
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining (2020, methodology): “If the optimization model is a Gaussian process, the DGM can be viewed as a form of “extended kernel”, making LSO conceptually related to deep kernel learning [63, 19].”