Publications (28)
Monotonic Gaussian Process Flow
Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek +1
The Robust Semantic Segmentation UNCV2023 Challenge Results
Xuanlong Yu, Yi Zuo, Zitao Wang +34
Compositional uncertainty in deep Gaussian processes
Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser +3
Hierarchical Subquery Evaluation for Active Learning on a Graph
Oisin Mac Aodha, Neill D. F. Campbell, Jan Kautz +1
Training VAEs Under Structured Residuals
Gara Dorta, Sara Vicente, Lourdes Agapito +2
Structured Uncertainty Similarity Score (SUSS): Learning a Probabilistic, Interpretable, Perceptual Metric Between Images
Paula Seidler, Neill D. F. Campbell, Ivor J A Simpson
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures
Andrew R. Lawrence, Carl Henrik Ek, Neill D. F. Campbell
The GAN that Warped: Semantic Attribute Editing with Unpaired Data
Gara Dorta, Sara Vicente, Neill D. F. Campbell +1
Gaussian Process Latent Variable Alignment Learning
Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell
ARC-Flow : Articulated, Resolution-Agnostic, Correspondence-Free Matching and Interpolation of 3D Shapes Under Flow Fields
Adam Hartshorne, Allen Paul, Tony Shardlow +1
Latent Gaussian Process Regression
Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek
Modulating Surrogates for Bayesian Optimization
Erik Bodin, Markus Kaiser, Ieva Kazlauskaite +3
Sequence Alignment with Dirichlet Process Mixtures
Ieva Kazlauskaite, Ivan Ustyuzhaninov, Carl Henrik Ek +1
Responsive Action-based Video Synthesis
Corneliu Ilisescu, Halil Aytac Kanaci, Matteo Romagnoli +2
Compressed Sensing MRI Reconstruction Regularized by VAEs with Structured Image Covariance
Margaret Duff, Ivor J. A. Simpson, Matthias J. Ehrhardt +1
Understanding Training-Data Leakage from Gradients in Neural Networks for Image Classification
Cangxiong Chen, Neill D. F. Campbell
DiverseNet: When One Right Answer is not Enough
Michael Firman, Neill D. F. Campbell, Lourdes Agapito +1
Likelihood-based Out-of-Distribution Detection with Denoising Diffusion Probabilistic Models
Joseph Goodier, Neill D. F. Campbell
Analysing Training-Data Leakage from Gradients through Linear Systems and Gradient Matching
Cangxiong Chen, Neill D. F. Campbell
Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation
Alessandro Di Martino, Erik Bodin, Carl Henrik Ek +1
Aligned Multi-Task Gaussian Process
Olga Mikheeva, Ieva Kazlauskaite, Adam Hartshorne +3
Black-box density function estimation using recursive partitioning
Erik Bodin, Zhenwen Dai, Neill D. F. Campbell +1
Regularising Inverse Problems with Generative Machine Learning Models
Margaret Duff, Neill D. F. Campbell, Matthias J. Ehrhardt
Gaussian Process Diffeomorphic Statistical Shape Modelling Outperforms Angle-Based Methods for Assessment of Hip Dysplasia
Allen Paul, George Grammatopoulos, Adwaye Rambojun +3
Learning Structured Gaussians to Approximate Deep Ensembles
Ivor J. A. Simpson, Sara Vicente, Neill D. F. Campbell
Structured SIR: Efficient and Expressive Importance-Weighted Inference for High-Dimensional Image Registration
Ivor J. A. Simpson, Neill D. F. Campbell
Nonparametric Inference for Auto-Encoding Variational Bayes
Erik Bodin, Iman Malik, Carl Henrik Ek +1
Structured Uncertainty Prediction Networks
Gara Dorta, Sara Vicente, Lourdes Agapito +2