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About Deep Gaussian Covariance Network
This document proposes a Deep Gaussian Covariance Network (DGCN) framework that uses a deep neural network to learn input-dependent, non-stationary hyperparameters (length scales and noise variances) for Gaussian processes. This allows the Gaussian process to have different covariance functions and hyperparameters depending on the location in input space, improving predictions when training data is non-uniformly distributed. The DGCN can combine multiple covariance functions and is flexible for extensions like local mixtures of experts or sequential problems. It also enables batch and online training of large datasets with Gaussian processes.
Detailed Information
| Author: | ['aa'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 14 |
| Language: | English |
| Format: | |
| Price: | FREE |
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