Neill D. F. Campbell | DeepAI

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Featured Co-authors

Jan Kautz
134 publications
Hedvig Kjellström
44 publications
Oisin Mac Aodha
34 publications
Lourdes Agapito
28 publications
Carl Henrik Ek
28 publications
Matthias J. Ehrhardt
22 publications
Zhenwen Dai
22 publications
Gabriel J. Brostow
18 publications
Ieva Kazlauskaite
12 publications
Ivan Ustyuzhaninov
9 publications
Michael Firman
9 publications

∙ 10/26/2022

Compressed Sensing MRI Reconstruction Regularized by VAEs with Structured Image Covariance

Learned regularization for MRI reconstruction can provide complex data-d...

0 Margaret Duff, et al. ∙

∙ 03/29/2022

Learning Structured Gaussians to Approximate Deep Ensembles

This paper proposes using a sparse-structured multivariate Gaussian to p...

0 Ivor J. A. Simpson, et al. ∙

∙ 11/19/2021

Understanding Training-Data Leakage from Gradients in Neural Networks for Image Classification

Federated learning of deep learning models for supervised tasks, e.g. im...

0 Cangxiong Chen, et al. ∙

∙ 10/29/2021

Aligned Multi-Task Gaussian Process

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

0 Olga Mikheeva, et al. ∙

∙ 07/22/2021

Regularising Inverse Problems with Generative Machine Learning Models

Deep neural network approaches to inverse imaging problems have produced...

8 Margaret Duff, 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. ∙

∙ 08/24/2020

DiverseNet: When One Right Answer is not Enough

Many structured prediction tasks in machine vision have a collection of ...

32 Michael Firman, 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 11/30/2018

The GAN that Warped: Semantic Attribute Editing with Unpaired Data

Deep neural networks have recently been used to edit images with great s...

12 Garoe Dorta, 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. ∙

∙ 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. ∙

∙ 04/03/2018

Training VAEs Under Structured Residuals

Variational auto-encoders (VAEs) are a popular and powerful deep generat...

0 Garoe Dorta, 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. ∙

∙ 02/20/2018

Structured Uncertainty Prediction Networks

This paper is the first work to propose a network to predict a structure...

0 Garoe Dorta, 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. ∙

∙ 07/18/2017

Latent Gaussian Process Regression

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

0 Erik Bodin, et al. ∙

∙ 05/20/2017

Responsive Action-based Video Synthesis

We propose technology to enable a new medium of expression, where video ...

0 Corneliu Ilisescu, et al. ∙

∙ 04/30/2015

Hierarchical Subquery Evaluation for Active Learning on a Graph

To train good supervised and semi-supervised object classifiers, it is c...

0 Oisin Mac Aodha, et al. ∙

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