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Found 7 papers, 3 papers with code
Making Differentiable Architecture Search less local
Neural architecture search (NAS) is a recent methodology for automating the design of neural network architectures.
Black-box density function estimation using recursive partitioning
We present a novel approach to Bayesian inference and general Bayesian computation that is defined through a sequential decision loop.
Compositional uncertainty in deep Gaussian processes
Similarly, deep Gaussian processes (DGPs) should allow us to compute a posterior distribution of compositions of multiple functions giving rise to the observations.
Modulating Surrogates for Bayesian Optimization
Bayesian optimization (BO) methods often rely on the assumption that the objective function is well-behaved, but in practice, this is seldom true for real-world objectives even if noise-free observations can be collected.
Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation
The shape of an object is an important characteristic for many vision problems such as segmentation, detection and tracking.
Nonparametric Inference for Auto-Encoding Variational Bayes
We would like to learn latent representations that are low-dimensional and highly interpretable.
Latent Gaussian Process Regression
We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary multi-modal processes using GPs.
