Search Results for author: Carl Henrik Ek

Found 25 papers, 6 papers with code

Aligned Multi-Task Gaussian Process

no code implementations29 Oct 2021 Olga Mikheeva, Ieva Kazlauskaite, Adam Hartshorne, Hedvig Kjellström, Carl Henrik Ek, Neill D. F. Campbell

Building on the previous work by Kazlauskaiteet al. [2019], we include a separate monotonic warp of the input data to model temporal misalignment.

Bayesian Inference Gaussian Processes +2

Deep Neural Networks as Point Estimates for Deep Gaussian Processes

no code implementations NeurIPS 2021 Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande

This results in models that can either be seen as neural networks with improved uncertainty prediction or deep Gaussian processes with increased prediction accuracy.

Bayesian Inference Gaussian Processes

Black-box density function estimation using recursive partitioning

1 code implementation26 Oct 2020 Erik Bodin, Zhenwen Dai, Neill D. F. Campbell, Carl Henrik Ek

We present a novel approach to Bayesian inference and general Bayesian computation that is defined through a sequential decision loop.

Bayesian Inference

Bayesian nonparametric shared multi-sequence time series segmentation

no code implementations27 Jan 2020 Olga Mikheeva, Ieva Kazlauskaite, Hedvig Kjellström, Carl Henrik Ek

In this paper, we introduce a method for segmenting time series data using tools from Bayesian nonparametrics.

Time Series

Compositional uncertainty in deep Gaussian processes

1 code implementation17 Sep 2019 Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek

Similarly, deep Gaussian processes (DGPs) should allow us to compute a posterior distribution of compositions of multiple functions giving rise to the observations.

Bayesian Inference Gaussian Processes +1

Modulating Surrogates for Bayesian Optimization

no code implementations ICML 2020 Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill D. F. Campbell, Carl Henrik Ek

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 Processes

Monotonic Gaussian Process Flow

1 code implementation30 May 2019 Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell

We propose a new framework for imposing monotonicity constraints in a Bayesian nonparametric setting based on numerical solutions of stochastic differential equations.

Gaussian Processes Time Series

Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation

1 code implementation13 Dec 2018 Alessandro Di Martino, Erik Bodin, Carl Henrik Ek, Neill D. F. Campbell

The shape of an object is an important characteristic for many vision problems such as segmentation, detection and tracking.

Sequence Alignment with Dirichlet Process Mixtures

no code implementations26 Nov 2018 Ieva Kazlauskaite, Ivan Ustyuzhaninov, Carl Henrik Ek, Neill D. F. Campbell

We present a probabilistic model for unsupervised alignment of high-dimensional time-warped sequences based on the Dirichlet Process Mixture Model (DPMM).

Gaussian Processes

Data Association with Gaussian Processes

no code implementations16 Oct 2018 Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek

The data association problem is concerned with separating data coming from different generating processes, for example when data come from different data sources, contain significant noise, or exhibit multimodality.

Gaussian Processes Variational Inference

DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures

no code implementations12 Jul 2018 Andrew R. Lawrence, Carl Henrik Ek, Neill D. F. Campbell

We present a non-parametric Bayesian latent variable model capable of learning dependency structures across dimensions in a multivariate setting.

Gaussian Process Latent Variable Alignment Learning

1 code implementation7 Mar 2018 Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell

We present a model that can automatically learn alignments between high-dimensional data in an unsupervised manner.

Nonparametric Inference for Auto-Encoding Variational Bayes

no code implementations18 Dec 2017 Erik Bodin, Iman Malik, Carl Henrik Ek, Neill D. F. Campbell

We would like to learn latent representations that are low-dimensional and highly interpretable.

Bayesian Alignments of Warped Multi-Output Gaussian Processes

no code implementations NeurIPS 2018 Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek

We apply the method to the real-world problem of finding common structure in the sensor data of wind turbines introduced by the underlying latent and turbulent wind field.

Gaussian Processes Time Series

Neural Translation of Musical Style

1 code implementation11 Aug 2017 Iman Malik, Carl Henrik Ek

Music is an expressive form of communication often used to convey emotion in scenarios where "words are not enough".


Latent Gaussian Process Regression

no code implementations18 Jul 2017 Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek

We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary multi-modal processes using GPs.

Manifold Alignment Determination: finding correspondences across different data views

no code implementations12 Jan 2017 Andreas Damianou, Neil D. Lawrence, Carl Henrik Ek

We present Manifold Alignment Determination (MAD), an algorithm for learning alignments between data points from multiple views or modalities.

Diagnostic Prediction Using Discomfort Drawings with IBTM

no code implementations27 Jul 2016 Cheng Zhang, Hedvig Kjellstrom, Carl Henrik Ek, Bo C. Bertilson

The positive result indicates a significant potential of machine learning to be used for parts of the pain diagnostic process and to be a decision support system for physicians and other health care personnel.

Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model

no code implementations30 Jun 2016 Fariba Yousefi, Zhenwen Dai, Carl Henrik Ek, Neil Lawrence

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.

Variational Inference

Inter-Battery Topic Representation Learning

no code implementations19 May 2016 Cheng Zhang, Hedvig Kjellstrom, Carl Henrik Ek

The structured representation leads to a model that marries benefits traditionally associated with a discriminative approach, such as feature selection, with those of a generative model, such as principled regularization and ability to handle missing data.

Feature Selection Representation Learning +1

On a Family of Decomposable Kernels on Sequences

no code implementations26 Jan 2015 Andrea Baisero, Florian T. Pokorny, Carl Henrik Ek

In many applications data is naturally presented in terms of orderings of some basic elements or symbols.

Dynamic Time Warping General Classification

Persistent Evidence of Local Image Properties in Generic ConvNets

no code implementations24 Nov 2014 Ali Sharif Razavian, Hossein Azizpour, Atsuto Maki, Josephine Sullivan, Carl Henrik Ek, Stefan Carlsson

Supervised training of a convolutional network for object classification should make explicit any information related to the class of objects and disregard any auxiliary information associated with the capture of the image or the variation within the object class.

General Classification

Factorized Topic Models

no code implementations15 Jan 2013 Cheng Zhang, Carl Henrik Ek, Andreas Damianou, Hedvig Kjellstrom

In this paper we present a modification to a latent topic model, which makes the model exploit supervision to produce a factorized representation of the observed data.

General Classification Topic Models +1

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