Search Results for author: Mauricio A. Álvarez

Found 27 papers, 15 papers with code

Deep Latent Force Models: ODE-based Process Convolutions for Bayesian Deep Learning

no code implementations24 Nov 2023 Thomas Baldwin-McDonald, Mauricio A. Álvarez

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.

Time Series Uncertainty Quantification +1

Shallow and Deep Nonparametric Convolutions for Gaussian Processes

1 code implementation17 Jun 2022 Thomas M. McDonald, Magnus Ross, Michael T. Smith, Mauricio A. Álvarez

A key challenge in the practical application of Gaussian processes (GPs) is selecting a proper covariance function.

Gaussian Processes

Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional Autoencoder

1 code implementation29 Mar 2022 Matthew Lyon, Paul Armitage, Mauricio A. Álvarez

In this work we develop a 3D recurrent convolutional neural network (RCNN) capable of super-resolving dMRI volumes in the angular (q-space) domain.

Super-Resolution

Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features

1 code implementation NeurIPS 2021 Thomas M. McDonald, Mauricio A. Álvarez

Effectively modeling phenomena present in highly nonlinear dynamical systems whilst also accurately quantifying uncertainty is a challenging task, which often requires problem-specific techniques.

Bayesian Inference Gaussian Processes +4

Learning Nonparametric Volterra Kernels with Gaussian Processes

1 code implementation NeurIPS 2021 Magnus Ross, Michael T. Smith, Mauricio A. Álvarez

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.

Gaussian Processes Numerical Integration +2

Recyclable Gaussian Processes

1 code implementation6 Oct 2020 Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez

We present a new framework for recycling independent variational approximations to Gaussian processes.

Gaussian Processes regression

A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process Model

1 code implementation22 Nov 2019 Juan-José Giraldo, Mauricio A. Álvarez

Furthermore, in this work we introduce an extension of the heterogeneous multi-output model, where its latent functions are drawn from convolution processes.

Gaussian Processes Variational Inference

Continual Multi-task Gaussian Processes

2 code implementations31 Oct 2019 Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez

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.

Bayesian Inference Continual Learning +3

Multi-task Learning for Aggregated Data using Gaussian Processes

2 code implementations NeurIPS 2019 Fariba Yousefi, Michael Thomas Smith, Mauricio A. Álvarez

Our model represents each task as the linear combination of the realizations of latent processes that are integrated at a different scale per task.

Air Pollution Prediction Epidemiology +2

Black-Box Inference for Non-Linear Latent Force Models

no code implementations21 Jun 2019 Wil O. C. Ward, Tom Ryder, Dennis Prangle, Mauricio A. Álvarez

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.

Gaussian Processes Variational Inference

Variational bridge constructs for approximate Gaussian process regression

no code implementations7 Jan 2019 Wil O. C. Ward, Mauricio A. Álvarez

We show that the approach extends easily to non-linear dynamics and discuss extensions to which the approach can be easily applied.

regression Variational Inference

Non-linear process convolutions for multi-output Gaussian processes

no code implementations10 Oct 2018 Mauricio A. Álvarez, Wil O. C. Ward, Cristian Guarnizo

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.

Gaussian Processes

Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processes

1 code implementation18 May 2018 Cristian Guarnizo, Mauricio A. Álvarez

A latent force model is a Gaussian process with a covariance function inspired by a differential operator.

Gaussian Processes

Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems

no code implementations15 Sep 2017 Simo Särkkä, Mauricio A. Álvarez, Neil D. Lawrence

This article is concerned with learning and stochastic control in physical systems which contain unknown input signals.

Gaussian Processes

Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes

2 code implementations NeurIPS 2017 Zhenwen Dai, Mauricio A. Álvarez, Neil D. Lawrence

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.

Gaussian Processes Variational Inference

Sparse Linear Models applied to Power Quality Disturbance Classification

no code implementations23 Nov 2015 Andrés F. López-Lopera, Mauricio A. Álvarez, Ávaro A. Orozco

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.

Classification General Classification

Switched latent force models for reverse-engineering transcriptional regulation in gene expression data

1 code implementation23 Nov 2015 Andrés F. López-Lopera, Mauricio A. Álvarez

To survive environmental conditions, cells transcribe their response activities into encoded mRNA sequences in order to produce certain amounts of protein concentrations.

A Parzen-based distance between probability measures as an alternative of summary statistics in Approximate Bayesian Computation

no code implementations30 Mar 2015 Carlos D. Zuluaga, Edgar A. Valencia, Mauricio A. Álvarez

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.

Bayesian Inference

Indian Buffet process for model selection in convolved multiple-output Gaussian processes

1 code implementation22 Mar 2015 Cristian Guarnizo, Mauricio A. Álvarez

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.

Gaussian Processes Model Selection +1

Discriminative training for Convolved Multiple-Output Gaussian processes

no code implementations7 Feb 2015 Sebastián Gómez-González, Mauricio A. Álvarez, Hernán Felipe García

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.

Activity Recognition Emotion Recognition +4

Linear Latent Force Models using Gaussian Processes

no code implementations13 Jul 2011 Mauricio A. Álvarez, David Luengo, Neil D. Lawrence

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.

Gaussian Processes

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