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Found 5 papers, 2 papers with code
Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains
Multi-task Gaussian process (MTGP) is a well-known non-parametric Bayesian model for learning correlated tasks effectively by transferring knowledge across tasks.
Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalization
Multi-task regression attempts to exploit the task similarity in order to achieve knowledge transfer across related tasks for performance improvement.
Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems
Thereafter, we first investigate the methodological characteristics of the proposed deep probabilistic sequence model on toy cases, and then comprehensively demonstrate the superiority of our model against existing deep probabilistic SSM models through extensive numerical experiments on eight system identification benchmarks from various dynamic systems.
Modulating Scalable Gaussian Processes for Expressive Statistical Learning
For a learning task, Gaussian process (GP) is interested in learning the statistical relationship between inputs and outputs, since it offers not only the prediction mean but also the associated variability.
Deep Latent-Variable Kernel Learning
Deep kernel learning (DKL) leverages the connection between Gaussian process (GP) and neural networks (NN) to build an end-to-end, hybrid model.
