166 citations · 309 across the 26 of their papers we have counts for
31 papers
Improving Ensemble Filters with Flow Matching
Haoyuan Chen, Alexandre Thiéry
Data assimilation estimates a dynamical state from partial and noisy observations. Classical ensemble filters are efficient but restrict analysis updates through finite sample cova…
A Generalized & Robust Framework For Timestamp Supervision in Temporal Action Segmentation
Rahul Rahaman, Dipika Singhania, Alexandre Thiery +1
In temporal action segmentation, Timestamp supervision requires only a handful of labelled frames per video sequence. For unlabelled frames, previous works rely on assigning hard l…
Computational Doob's h-transforms for Online Filtering of Discretely Observed Diffusions
Nicolas Chopin, Andras Fulop, Jeremy Heng +1
This paper is concerned with online filtering of discretely observed nonlinear diffusion processes. Our approach is based on the fully adapted auxiliary particle filter, which invo…
Conditional sequential Monte Carlo in high dimensions
Axel Finke, Alexandre H. Thiery
The iterated conditional sequential Monte Carlo (i-CSMC) algorithm from Andrieu, Doucet and Holenstein (2010) is an MCMC approach for efficiently sampling from the joint posterior…
Pretrained equivariant features improve unsupervised landmark discovery
Rahul Rahaman, Atin Ghosh, Alexandre H. Thiery
Locating semantically meaningful landmark points is a crucial component of a large number of computer vision pipelines. Because of the small number of available datasets with groun…
On Data-Augmentation and Consistency-Based Semi-Supervised Learning
Atin Ghosh, Alexandre H. Thiery
Recently proposed consistency-based Semi-Supervised Learning (SSL) methods such as the -model, temporal ensembling, the mean teacher, or the virtual adversarial training, have a…
Uncertainty Quantification and Deep Ensembles
Rahul Rahaman, Alexandre H. Thiery
Deep Learning methods are known to suffer from calibration issues: they typically produce over-confident estimates. These problems are exacerbated in the low data regime. Although…
Manifold lifting: scaling MCMC to the vanishing noise regime
Khai Xiang Au, Matthew M. Graham, Alexandre H. Thiery
Standard Markov chain Monte Carlo methods struggle to explore distributions that are concentrated in the neighbourhood of low-dimensional structures. These pathologies naturally oc…
Towards Label-Free 3D Segmentation of Optical Coherence Tomography Images of the Optic Nerve Head Using Deep Learning
Sripad Krishna Devalla, Tan Hung Pham, Satish Kumar Panda +16
Since the introduction of optical coherence tomography (OCT), it has been possible to study the complex 3D morphological changes of the optic nerve head (ONH) tissues that occur al…
Manifold Markov chain Monte Carlo methods for Bayesian inference in diffusion models
Matthew M. Graham, Alexandre H. Thiery, Alexandros Beskos
Bayesian inference for nonlinear diffusions, observed at discrete times, is a challenging task that has prompted the development of a number of algorithms, mainly within the comput…
DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images
Haris Cheong, Sripad Krishna Devalla, Tan Hung Pham +9
Purpose: To remove retinal shadows from optical coherence tomography (OCT) images of the optic nerve head(ONH). Methods:2328 OCT images acquired through the center of the ONH using…
Sequential Ensemble Transform for Bayesian Inverse Problems
Aaron Myers, Alexandre H. Thiery, Kainan Wang +1
We present the Sequential Ensemble Transform (SET) method, an approach for generating approximate samples from a Bayesian posterior distribution. The method explores the posterior…
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images
Tan Hung Pham, Sripad Krishna Devalla, Aloysius Ang +6
Accurate isolation and quantification of intraocular dimensions in the anterior segment (AS) of the eye using optical coherence tomography (OCT) images is important in the diagnosi…
On importance-weighted autoencoders
Axel Finke, Alexandre H. Thiery
The importance weighted autoencoder (IWAE) (Burda et al., 2016) is a popular variational-inference method which achieves a tighter evidence bound (and hence a lower bias) than stan…
A scalable optimal-transport based local particle filter
Matthew M. Graham, Alexandre H. Thiery
Filtering in spatially-extended dynamical systems is a challenging problem with significant practical applications such as numerical weather prediction. Particle filters allow asym…
Particle filter efficiency under limited communication
Deborshee Sen
Sequential Monte Carlo methods are typically not straightforward to implement on parallel architectures. This is because standard resampling schemes involve communication between a…
A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head
Sripad Krishna Devalla, Giridhar Subramanian, Tan Hung Pham +7
Purpose: To develop a deep learning approach to de-noise optical coherence tomography (OCT) B-scans of the optic nerve head (ONH). Methods: Volume scans consisting of 97 horizontal…
DRUNET: A Dilated-Residual U-Net Deep Learning Network to Digitally Stain Optic Nerve Head Tissues in Optical Coherence Tomography Images
Sripad Krishna Devalla, Prajwal K. Renukanand, Bharathwaj K. Sreedhar +8
Given that the neural and connective tissues of the optic nerve head (ONH) exhibit complex morphological changes with the development and progression of glaucoma, their simultaneou…
A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head
Sripad Krishna Devalla, Jean-Martial Mari, Tin A. Tun +4
Purpose: To develop a deep learning approach to digitally-stain optical coherence tomography (OCT) images of the optic nerve head (ONH). Methods: A horizontal B-scan was acquired t…
A Discrete Bouncy Particle Sampler
Chris Sherlock, Alexandre H. Thiery
Most Markov chain Monte Carlo methods operate in discrete time and are reversible with respect to the target probability. Nevertheless, it is now understood that the use of non-rev…
Pseudo-marginal Metropolis--Hastings using averages of unbiased estimators
Chris Sherlock, Alexandre Thiery, Anthony Lee
We consider a pseudo-marginal Metropolis--Hastings kernel that is constructed using an average of exchangeable random variables, as well as an analogous kernel that…
Lévy statistics of interacting Rydberg gases
Thibault Vogt, Jingshan Han, Alexandre Thiery +1
A statistical analysis of the laser excitation of cold and randomly distributed atoms to Rydberg states is developed. We first demonstrate with a hard ball model that the distribut…
On Coupling Particle Filter Trajectories
Deborshee Sen, Alexandre Thiery, Ajay Jasra
Particle filters are a powerful and flexible tool for performing inference on state-space models. They involve a collection of samples evolving over time through a combination of s…
Asymptotic Analysis of the Random-Walk Metropolis Algorithm on Ridged Densities
Alexandros Beskos, Gareth Roberts, Alexandre Thiery +1
In this paper we study the asymptotic behavior of the Random-Walk Metropolis algorithm on probability densities with two different `scales', where most of the probability mass is d…