Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
复制标题
DOI：
10.48550/arxiv.2302.04534
复制
发表时间：
2023-02
期刊：
影响因子：
--
通讯作者：
Ba-Hien Tran;B. Shahbaba;S. Mandt;M. Filippone
中科院分区：
文献类型：
--
作者：
Ba-Hien Tran;B. Shahbaba;S. Mandt;M. Filippone
We present a fully Bayesian autoencoder model that treats both local latent variables and global decoder parameters in a Bayesian fashion. This approach allows for flexible priors and posterior approximations while keeping the inference costs low. To achieve this, we introduce an amortized MCMC approach by utilizing an implicit stochastic network to learn sampling from the posterior over local latent variables. Furthermore, we extend the model by incorporating a Sparse Gaussian Process prior over the latent space, allowing for a fully Bayesian treatment of inducing points and kernel hyperparameters and leading to improved scalability. Additionally, we enable Deep Gaussian Process priors on the latent space and the handling of missing data. We evaluate our model on a range of experiments focusing on dynamic representation learning and generative modeling, demonstrating the strong performance of our approach in comparison to existing methods that combine Gaussian Processes and autoencoders.
