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Found 26 papers, 15 papers with code
Deep Latent Force Models: ODE-based Process Convolutions for Bayesian Deep Learning
Modelling the behaviour of highly nonlinear dynamical systems with robust uncertainty quantification is a challenging task which typically requires approaches specifically designed to address the problem at hand.
Shallow and Deep Nonparametric Convolutions for Gaussian Processes
A key challenge in the practical application of Gaussian processes (GPs) is selecting a proper covariance function.
Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional Autoencoder
In this work we develop a 3D recurrent convolutional neural network (RCNN) capable of super-resolving dMRI volumes in the angular (q-space) domain.
Modular Gaussian Processes for Transfer Learning
We present a framework for transfer learning based on modular variational Gaussian processes (GP).
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features
Effectively modeling phenomena present in highly nonlinear dynamical systems whilst also accurately quantifying uncertainty is a challenging task, which often requires problem-specific techniques.
Learning Nonparametric Volterra Kernels with Gaussian Processes
When the input function to the operator is unobserved and has a GP prior, the NVKM constitutes a powerful method for both single and multiple output regression, and can be viewed as a nonlinear and nonparametric latent force model.
Recyclable Gaussian Processes
We present a new framework for recycling independent variational approximations to Gaussian processes.
A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process Model
Furthermore, in this work we introduce an extension of the heterogeneous multi-output model, where its latent functions are drawn from convolution processes.
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.
Multi-task Learning for Aggregated Data using Gaussian Processes
Our model represents each task as the linear combination of the realizations of latent processes that are integrated at a different scale per task.
Black-Box Inference for Non-Linear Latent Force Models
Latent force models are systems whereby there is a mechanistic model describing the dynamics of the system state, with some unknown forcing term that is approximated with a Gaussian process.
Variational bridge constructs for approximate Gaussian process regression
We show that the approach extends easily to non-linear dynamics and discuss extensions to which the approach can be easily applied.
Sparse Gaussian Process Audio Source Separation Using Spectrum Priors in the Time-Domain
As a result, source separation GP models have been restricted to the analysis of short audio frames.
Non-linear process convolutions for multi-output Gaussian processes
We provide closed-form expressions for the mean function and the covariance function of the approximated Gaussian process at the output of the Volterra series.
Heterogeneous Multi-output Gaussian Process Prediction
We present a novel extension of multi-output Gaussian processes for handling heterogeneous outputs.
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processes
A latent force model is a Gaussian process with a covariance function inspired by a differential operator.
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems
This article is concerned with learning and stochastic control in physical systems which contain unknown input signals.
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
Often in machine learning, data are collected as a combination of multiple conditions, e. g., the voice recordings of multiple persons, each labeled with an ID.
A Three Spatial Dimension Wave Latent Force Model for Describing Excitation Sources and Electric Potentials Produced by Deep Brain Stimulation
The electric potential produced by a time-varying source was predicted using proposed model.
Short-term time series prediction using Hilbert space embeddings of autoregressive processes
Linear autoregressive models serve as basic representations of discrete time stochastic processes.
Switched latent force models for reverse-engineering transcriptional regulation in gene expression data
To survive environmental conditions, cells transcribe their response activities into encoded mRNA sequences in order to produce certain amounts of protein concentrations.
Sparse Linear Models applied to Power Quality Disturbance Classification
The automatic recognition of PQ disturbances can be seen as a pattern recognition problem, in which different types of waveform distortion are differentiated based on their features.
A Parzen-based distance between probability measures as an alternative of summary statistics in Approximate Bayesian Computation
Recently, a nonparametric ABC has been proposed, that uses a dissimilarity measure between discrete distributions based on empirical kernel embeddings as an alternative for summary statistics.
Indian Buffet process for model selection in convolved multiple-output Gaussian processes
We propose in this paper, the use of an Indian Buffet process as a way to perform model selection over the number of latent Gaussian processes.
Discriminative training for Convolved Multiple-Output Gaussian processes
A less explored facet of the multi-output Gaussian process is that it can be used as a generative model for vector-valued random fields in the context of pattern recognition.
Linear Latent Force Models using Gaussian Processes
Purely data driven approaches for machine learning present difficulties when data is scarce relative to the complexity of the model or when the model is forced to extrapolate.
