Carl Henrik Ek | DeepAI

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Featured Co-authors

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∙ 12/20/2022

Identifying latent distances with Finslerian geometry

Riemannian geometry provides powerful tools to explore the latent space ...

0 Alison Pouplin, et al. ∙

∙ 11/26/2022

Optimisation of a global climate model ensemble for prediction of extreme heat days

Adaptation-relevant predictions of climate change are often derived by c...

0 Mala Virdee, et al. ∙

∙ 10/29/2021

Aligned Multi-Task Gaussian Process

Multi-task learning requires accurate identification of the correlations...

0 Olga Mikheeva, et al. ∙

∙ 05/10/2021

Deep Neural Networks as Point Estimates for Deep Gaussian Processes

Deep Gaussian processes (DGPs) have struggled for relevance in applicati...

23 Vincent Dutordoir, et al. ∙

∙ 10/26/2020

Black-box density function estimation using recursive partitioning

We present a novel approach to Bayesian inference and general Bayesian c...

0 Erik Bodin, et al. ∙

∙ 01/27/2020

Bayesian nonparametric shared multi-sequence time series segmentation

In this paper, we introduce a method for segmenting time series data usi...

3 Olga Mikheeva, et al. ∙

∙ 09/17/2019

Compositional uncertainty in deep Gaussian processes

Gaussian processes (GPs) are nonparametric priors over functions, and fi...

13 Ivan Ustyuzhaninov, et al. ∙

∙ 07/10/2019

Interpretable Dynamics Models for Data-Efficient Reinforcement Learning

In this paper, we present a Bayesian view on model-based reinforcement l...

5 Markus Kaiser, et al. ∙

∙ 06/26/2019

Modulated Bayesian Optimization using Latent Gaussian Process Models

We present an approach to Bayesian Optimization that allows for robust s...

2 Erik Bodin, et al. ∙

∙ 05/30/2019

Monotonic Gaussian Process Flow

We propose a new framework of imposing monotonicity constraints in a Bay...

0 Ivan Ustyuzhaninov, et al. ∙

∙ 01/30/2019

Invariant Feature Mappings for Generalizing Affordance Understanding Using Regularized Metric Learning

This paper presents an approach for learning invariant features for obje...

0 Martin Hjelm, et al. ∙

∙ 12/13/2018

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

The shape of an object is an important characteristic for many vision pr...

14 Alessandro Di Martino, et al. ∙

∙ 11/26/2018

Sequence Alignment with Dirichlet Process Mixtures

We present a probabilistic model for unsupervised alignment of high-dime...

0 Ieva Kazlauskaite, et al. ∙

∙ 10/16/2018

Multimodal Deep Gaussian Processes

We propose a novel Bayesian approach to modelling multimodal data genera...

0 Markus Kaiser, et al. ∙

∙ 07/12/2018

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

We present a non-parametric Bayesian latent variable model capable of le...

8 Andrew R. Lawrence, et al. ∙

∙ 05/18/2018

Model Inference with Stein Density Ratio Estimation

The Kullback-Leilber divergence from model to data is a classic goodness...

0 Song Liu, et al. ∙

∙ 03/07/2018

Gaussian Process Latent Variable Alignment Learning

We present a model that can automatically learn alignments between high-...

0 Ieva Kazlauskaite, et al. ∙

∙ 02/13/2018

Active Exploration Using Gaussian Random Fields and Gaussian Process Implicit Surfaces

In this work we study the problem of exploring surfaces and building com...

0 Sergio Caccamo, et al. ∙

∙ 12/18/2017

Nonparametric Inference for Auto-Encoding Variational Bayes

We would like to learn latent representations that are low-dimensional a...

0 Erik Bodin, et al. ∙

∙ 10/08/2017

Bayesian Alignments of Warped Multi-Output Gaussian Processes

We present a Bayesian extension to convolution processes which defines a...

0 Markus Kaiser, et al. ∙

∙ 08/11/2017

Neural Translation of Musical Style

Music is an expressive form of communication often used to convey emotio...

0 Iman Malik, et al. ∙

∙ 07/18/2017

Latent Gaussian Process Regression

We introduce Latent Gaussian Process Regression which is a latent variab...

0 Erik Bodin, et al. ∙

∙ 01/12/2017

Manifold Alignment Determination: finding correspondences across different data views

We present Manifold Alignment Determination (MAD), an algorithm for lear...

0 Andreas Damianou, et al. ∙

∙ 06/30/2016

Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model

Unsupervised learning on imbalanced data is challenging because, when gi...

0 Fariba Yousefi, et al. ∙

∙ 05/19/2016

Inter-Battery Topic Representation Learning

In this paper, we present the Inter-Battery Topic Model (IBTM). Our appr...

0 Cheng Zhang, et al. ∙

∙ 04/17/2016

Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis

Factor analysis aims to determine latent factors, or traits, which summa...

0 Andreas Damianou, et al. ∙

∙ 11/24/2014

Persistent Evidence of Local Image Properties in Generic ConvNets

Supervised training of a convolutional network for object classification...

0 Ali Sharif Razavian, et al. ∙

∙ 01/15/2013

Factorized Topic Models

In this paper we present a modification to a latent topic model, which m...

0 Cheng Zhang, et al. ∙