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10/26/2022
Compressed Sensing MRI Reconstruction Regularized by VAEs with Structured Image Covariance
Learned regularization for MRI reconstruction can provide complex data-d...
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03/29/2022
Learning Structured Gaussians to Approximate Deep Ensembles
This paper proposes using a sparse-structured multivariate Gaussian to p...
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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...
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10/29/2021
Aligned Multi-Task Gaussian Process
Multi-task learning requires accurate identification of the correlations...
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07/22/2021
Regularising Inverse Problems with Generative Machine Learning Models
Deep neural network approaches to inverse imaging problems have produced...
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10/26/2020
Black-box density function estimation using recursive partitioning
We present a novel approach to Bayesian inference and general Bayesian c...
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08/24/2020
DiverseNet: When One Right Answer is not Enough
Many structured prediction tasks in machine vision have a collection of ...
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09/17/2019
Compositional uncertainty in deep Gaussian processes
Gaussian processes (GPs) are nonparametric priors over functions, and fi...
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06/26/2019
Modulated Bayesian Optimization using Latent Gaussian Process Models
We present an approach to Bayesian Optimization that allows for robust s...
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05/30/2019
Monotonic Gaussian Process Flow
We propose a new framework of imposing monotonicity constraints in a Bay...
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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...
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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...
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11/26/2018
Sequence Alignment with Dirichlet Process Mixtures
We present a probabilistic model for unsupervised alignment of high-dime...
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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...
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04/03/2018
Training VAEs Under Structured Residuals
Variational auto-encoders (VAEs) are a popular and powerful deep generat...
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03/07/2018
Gaussian Process Latent Variable Alignment Learning
We present a model that can automatically learn alignments between high-...
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02/20/2018
Structured Uncertainty Prediction Networks
This paper is the first work to propose a network to predict a structure...
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12/18/2017
Nonparametric Inference for Auto-Encoding Variational Bayes
We would like to learn latent representations that are low-dimensional a...
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07/18/2017
Latent Gaussian Process Regression
We introduce Latent Gaussian Process Regression which is a latent variab...
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05/20/2017
Responsive Action-based Video Synthesis
We propose technology to enable a new medium of expression, where video ...
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04/30/2015
