Deep Gaussian Processes
Deep Gaussian Processes
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Deep Gaussian Processes (DGPs) are probabilistic non-parametric models which extend the classic Gaussian Process (GP) by introducing multiple layers of latent variables. They allow for the combination of feature-engineering and learning of deep representations, providing an expressive and flexible model to represent complex data. A DGP consists of a sequence of GP layers, with the output of one layer becoming the input of the next. This makes it possible to learn complex non-linear correlations between data points. Additionally, the uncertainty of the predictions can be accurately estimated, making DGPs a valuable tool for Bayesian inference.
... Deep neural networks as point estimates for deep Gaussian processes. Advances in Neural Information Processing Systems, 34. ...
... Deep Infinite Mixture of Gaussian Processes (DIM-GP) ist unser selbstentwickelter Algorithmus für Machine Learning. Dabei wird eine einzigartige Kombination von neuronalen Netzen (Deep Learning) und Gaußprozessen verwendet. ...
... Proven track record in advanced topics of Machine Learning (e.g., Bayesian inference, hierarchical models, deep learning, Gaussian processes, causal inference, graph theory, etc.). ...
... Specifically, Javier’s research spans Bayesian deep learning, Gaussian processes, causal inference and interpretable machine learning. ...
... Robust Deep Gaussian Processes by Jeremias ...
... In practice, it is more common to use deep Gaussian Processes for automatic kernel design, which optimizes the choice of covariance function that is appropriate for your data through training. ...
... Multiscale Prediction of Failure EventsUse Predictive Models (SVM, DynamicTreed Gaussian Processes, Deep Boltzmann Machines, Logistic Model Trees) ...
... Multiscale Prediction of Failure EventsUse Predictive Models (SVM, DynamicTreed Gaussian Processes, Deep Boltzmann Machines, Logistic Model Trees) ...
... His research focuses on Gaussian processes, Bayesian deep learning, dynamical models and reinforcement learning with applications in bio- and chemoinformatics and robotics. He has 32 peer-reviewed publications with an H-index of 17. His publication record contains recent contributions ...
... 14:44 - Deep State-Space Gaussian Processes - Zheng Zhao (Aalto University) [link to presentation] ...
... Conference on Artificial Intelligence and Statistics (AISTATS) – 2020 Compositional uncertainty in deep Gaussian processes – UAI – 2020 Modulating Surrogates for Bayesian Optimization – ICML – 2020 Gaussian Process Latent Variable Alignment Learning – International Conference on Artificial ...
