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Found 14 papers, 2 papers with code
Overlapping Mixtures of Gaussian Processes for the Data Association Problem
In this work we introduce a mixture of GPs to address the data association problem, i. e. to label a group of observations according to the sources that generated them.
Gaussian Process Models with Parallelization and GPU acceleration
In this work, we present an extension of Gaussian process (GP) models with sophisticated parallelization and GPU acceleration.
Spike and Slab Gaussian Process Latent Variable Models
The Gaussian process latent variable model (GP-LVM) is a popular approach to non-linear probabilistic dimensionality reduction.
Variational Auto-encoded Deep Gaussian Processes
We develop a scalable deep non-parametric generative model by augmenting deep Gaussian processes with a recognition model.
Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model
Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data.
Spatio-temporal Gaussian processes modeling of dynamical systems in systems biology
The spatio-temporal field of protein concentration and mRNA expression are reconstructed without explicitly solving the partial differential equation.
Parallelizable sparse inverse formulation Gaussian processes (SpInGP)
We propose a parallelizable sparse inverse formulation Gaussian process (SpInGP) for temporal models.
Intrinsic Gaussian processes on complex constrained domains
in-GPs respect the potentially complex boundary or interior conditions as well as the intrinsic geometry of the spaces.
Structured Variationally Auto-encoded Optimization
We tackle the problem of optimizing a black-box objective function defined over a highly-structured input space.
Continual Learning in Practice
This paper describes a reference architecture for self-maintaining systems that can learn continually, as data arrives.
Deep Gaussian Processes for Multi-fidelity Modeling
Multi-fidelity methods are prominently used when cheaply-obtained, but possibly biased and noisy, observations must be effectively combined with limited or expensive true data in order to construct reliable models.
Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients
Conventional models tend to forget the knowledge of previous tasks while learning a new task, a phenomenon known as catastrophic forgetting.
Meta-Surrogate Benchmarking for Hyperparameter Optimization
Despite the recent progress in hyperparameter optimization (HPO), available benchmarks that resemble real-world scenarios consist of a few and very large problem instances that are expensive to solve.
