Search Results for author:
Found 27 papers, 9 papers with code
Likelihood-based Out-of-Distribution Detection with Denoising Diffusion Probabilistic Models
We present results that are comparable to state-of-the-art Out-of-Distribution detection methods with generative models.
The Robust Semantic Segmentation UNCV2023 Challenge Results
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023.
Compressed Sensing MRI Reconstruction Regularized by VAEs with Structured Image Covariance
The covariance can model changing uncertainty dependencies caused by structure in the image, such as edges or objects, and provides a new distance metric from the manifold of learned images.
Analysing Training-Data Leakage from Gradients through Linear Systems and Gradient Matching
As a result, we are able to partially attribute the leakage of the training data in a deep network to its architecture.
Learning Structured Gaussians to Approximate Deep Ensembles
Similarly to distillation approaches, our single network is trained to maximise the probability of samples from pre-trained probabilistic models, in this work we use a fixed ensemble of networks.
Shooting Schrödinger’s Cat
We present a variational inference scheme to learn a model that solves the Schrödinger Bridge Problem (SBP).
Understanding Training-Data Leakage from Gradients in Neural Networks for Image Classification
Based on this formulation, we are able to attribute the potential leakage of the training data in a deep network to its architecture.
Aligned Multi-Task Gaussian Process
Building on the previous work by Kazlauskaiteet al. [2019], we include a separate monotonic warp of the input data to model temporal misalignment.
Regularising Inverse Problems with Generative Machine Learning Models
The success of generative regularisers depends on the quality of the generative model and so we propose a set of desired criteria to assess generative models and guide future research.
Black-box density function estimation using recursive partitioning
We present a novel approach to Bayesian inference and general Bayesian computation that is defined through a sequential decision loop.
DiverseNet: When One Right Answer is not Enough
For a single input, we learn to predict a range of possible answers.
Compositional uncertainty in deep Gaussian processes
Similarly, deep Gaussian processes (DGPs) should allow us to compute a posterior distribution of compositions of multiple functions giving rise to the observations.
Modulating Surrogates for Bayesian Optimization
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.
Monotonic Gaussian Process Flow
We propose a new framework for imposing monotonicity constraints in a Bayesian nonparametric setting based on numerical solutions of stochastic differential equations.
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 problems such as segmentation, detection and tracking.
The GAN that Warped: Semantic Attribute Editing with Unpaired Data
Deep neural networks have recently been used to edit images with great success, in particular for faces.
Sequence Alignment with Dirichlet Process Mixtures
We present a probabilistic model for unsupervised alignment of high-dimensional time-warped sequences based on the Dirichlet Process Mixture Model (DPMM).
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures
We present a non-parametric Bayesian latent variable model capable of learning dependency structures across dimensions in a multivariate setting.
Training VAEs Under Structured Residuals
This paper demonstrates a novel scheme to incorporate a structured Gaussian likelihood prediction network within the VAE that allows the residual correlations to be modeled.
Gaussian Process Latent Variable Alignment Learning
We present a model that can automatically learn alignments between high-dimensional data in an unsupervised manner.
Structured Uncertainty Prediction Networks
This paper is the first work to propose a network to predict a structured uncertainty distribution for a synthesized image.
Nonparametric Inference for Auto-Encoding Variational Bayes
We would like to learn latent representations that are low-dimensional and highly interpretable.
Latent Gaussian Process Regression
We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary multi-modal processes using GPs.
Direct, Dense, and Deformable: Template-Based Non-Rigid 3D Reconstruction From RGB Video
In contrast, our method makes use of a single RGB video as input; it can capture the deformations of generic shapes; and the depth estimation is dense, per-pixel and direct.
Modeling Object Appearance Using Context-Conditioned Component Analysis
In this work we remove the image space alignment limitations of existing subspace models by conditioning the models on a shape dependent context that allows for the complex, non-linear structure of the appearance of the visual object to be captured and shared.
Hierarchical Subquery Evaluation for Active Learning on a Graph
Under some specific circumstances, Expected Error Reduction has been one of the strongest-performing informativeness criteria for active learning.
Fully-Connected CRFs with Non-Parametric Pairwise Potential
Conditional Random Fields (CRFs) are used for diverse tasks, ranging from image denoising to object recognition.
