Reference from PhD Thesis:
- Auto-Encoding Variational Bayes
- Stochastic Backpropagation and Approximate Inference in Deep Generative Models
- Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions
- From Pixels to Torques: Policy Learning with Deep Dynamical Models
- Sparse Greedy Gaussian Process Regression
- Autoencoders, Unsupervised Learning, and Deep Architectures
- Gaussian processes autoencoder for dimensionality reduction
- Stacked Convolutional Auto-Encoders Hierarchical Feature Extraction
- Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
- PROPAGATION OF UNCERTAINTY IN BAYESIAN KERNEL MODELS – APPLICATION TO MULTIPLE-STEP AHEAD FORECASTING
- Gaussian Processes for Machine Learning (GPML) Toolbox
- Sparse Gaussian Processes using Pseudo-inputs
- Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion