Scalable Gaussian Process Variational Autoencoders
Scalable Gaussian Process Variational Autoencoders
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发表时间：
2020-10
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通讯作者：
Metod Jazbec;Vincent Fortuin;Michael Pearce;S. Mandt;Gunnar Rätsch
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作者：
Metod Jazbec;Vincent Fortuin;Michael Pearce;S. Mandt;Gunnar Rätsch
传统的变分自动编码器由于使用因子分解先验而无法对数据点之间的相关性进行建模。通过GP-VAE的摊销高斯过程推理在这方面有了显着的改进，但仍然受到精确GP推理固有复杂性的抑制。我们通过原则性稀疏推理方法提高了这些方法的可扩展性。我们提出了一个新的可扩展的GP-VAE模型，在运行时间和内存占用方面优于现有的方法，易于实现，并允许所有组件的联合端到端优化。
Conventional variational autoencoders fail in modeling correlations between data points due to their use of factorized priors. Amortized Gaussian process inference through GP-VAEs has led to significant improvements in this regard, but is still inhibited by the intrinsic complexity of exact GP inference. We improve the scalability of these methods through principled sparse inference approaches. We propose a new scalable GP-VAE model that outperforms existing approaches in terms of runtime and memory footprint, is easy to implement, and allows for joint end-to-end optimization of all components.