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Found 11 papers, 9 papers with code
Revisiting Active Sets for Gaussian Process Decoders
Decoders built on Gaussian processes (GPs) are enticing due to the marginalisation over the non-linear function space.
Laplacian Autoencoders for Learning Stochastic Representations
In this work, we present a Bayesian autoencoder for unsupervised representation learning, which is trained using a novel variational lower-bound of the autoencoder evidence.
Adaptive Cholesky Gaussian Processes
We present a method to approximate Gaussian process regression models for large datasets by considering only a subset of the data.
Modular Gaussian Processes for Transfer Learning
We present a framework for transfer learning based on modular variational Gaussian processes (GP).
Passive detection of behavioral shifts for suicide attempt prevention
More than one million people commit suicide every year worldwide.
Recyclable Gaussian Processes
We present a new framework for recycling independent variational approximations to Gaussian processes.
Multinomial Sampling for Hierarchical Change-Point Detection
Bayesian change-point detection, together with latent variable models, allows to perform segmentation over high-dimensional time-series.
Continual Multi-task Gaussian Processes
We then demonstrate that it is possible to derive GP models over many types of sequential observations, either discrete or continuous and amenable to stochastic optimization.
Continual Learning for Infinite Hierarchical Change-Point Detection
Change-point detection (CPD) aims to locate abrupt transitions in the generative model of a sequence of observations.
Change-Point Detection on Hierarchical Circadian Models
This paper addresses the problem of change-point detection on sequences of high-dimensional and heterogeneous observations, which also possess a periodic temporal structure.
Heterogeneous Multi-output Gaussian Process Prediction
We present a novel extension of multi-output Gaussian processes for handling heterogeneous outputs.
