
Blockterm Tensor Neural Networks
Deep neural networks (DNNs) have achieved outstanding performance in a w...
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Streaming Probabilistic Deep Tensor Factorization
Despite the success of existing tensor factorization methods, most of th...
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MultiFidelity Bayesian Optimization via Deep Neural Networks
Bayesian optimization (BO) is a popular framework to optimize blackbox ...
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Physics Regularized Gaussian Processes
We consider incorporating incomplete physics knowledge, expressed as dif...
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MultiFidelity HighOrder Gaussian Processes for Physical Simulation
The key task of physical simulation is to solve partial differential equ...
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Scalable Variational Gaussian Process Regression Networks
Gaussian process regression networks (GPRN) are powerful Bayesian models...
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Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications
Despite the wide implementation of machine learning (ML) techniques in t...
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Conditional Expectation Propagation
Expectation propagation (EP) is a powerful approximate inference algorit...
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Deep Coregionalization for the Emulation of SpatialTemporal Fields
Datadriven surrogate models are widely used for applications such as de...
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Learning Compact Recurrent Neural Networks with BlockTerm Tensor Decomposition
Recurrent Neural Networks (RNNs) are powerful sequence modeling tools. H...
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Asynchronous Distributed Variational Gaussian Processes for Regression
Gaussian processes (GPs) are powerful nonparametric function estimators...
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Distributed Flexible Nonlinear Tensor Factorization
Tensor factorization is a powerful tool to analyse multiway data. Compa...
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DinTucker: Scaling up Gaussian process models on multidimensional arrays with billions of elements
Infinite Tucker Decomposition (InfTucker) and random function prior mode...
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Supervised Heterogeneous Multiview Learning for Joint Association Study and Disease Diagnosis
Given genetic variations and various phenotypical traits, such as Magnet...
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Shandian Zhe
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