297 citations · 1.5k across the 199 of their papers we have counts for
28 papers · 1 filter
Maximum Entropy on the Mean and the Cramér Rate Function in Statistical Estimation and Inverse Problems: Properties, Models, and Algorithms
Yakov Vaisbourd, Rustum Choksi, Ariel Goodwin +2
We explore a method of statistical estimation called Maximum Entropy on the Mean (MEM) which is based on an information-driven criterion that quantifies the compliance of a given p…
Your diffusion model secretly knows the dimension of the data manifold
Jan Stanczuk, Georgios Batzolis, Teo Deveney +1
In this work, we propose a novel framework for estimating the dimension of the data manifold using a trained diffusion model. A diffusion model approximates the score function i.e.…
On Krylov Methods for Large Scale CBCT Reconstruction
Malena Sabate Landman, Ander Biguri, Sepideh Hatamikia +3
Krylov subspace methods are a powerful family of iterative solvers for linear systems of equations, which are commonly used for inverse problems due to their intrinsic regularizati…
TrafficCAM: A Versatile Dataset for Traffic Flow Segmentation
Zhongying Deng, Yanqi Chen, Lihao Liu +4
Traffic flow analysis is revolutionising traffic management. Qualifying traffic flow data, traffic control bureaus could provide drivers with real-time alerts, advising the fastest…
NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning
Zhongying Deng, Rihuan Ke, Carola-Bibiane Schonlieb +1
Semi-Supervised Learning (SSL) aims to learn a model using a tiny labeled set and massive amounts of unlabeled data. To better exploit the unlabeled data the latest SSL methods use…
SCOTCH and SODA: A Transformer Video Shadow Detection Framework
Lihao Liu, Jean Prost, Lei Zhu +4
Shadows in videos are difficult to detect because of the large shadow deformation between frames. In this work, we argue that accounting for shadow deformation is essential when de…
Navigating the challenges in creating complex data systems: a development philosophy
Sören Dittmer, Michael Roberts, Julian Gilbey +6
In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy a…
Robust Data-Driven Accelerated Mirror Descent
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang +2
Learning-to-optimize is an emerging framework that leverages training data to speed up the solution of certain optimization problems. One such approach is based on the classical mi…
Dynamical systems' based neural networks
Elena Celledoni, Davide Murari, Brynjulf Owren +2
Neural networks have gained much interest because of their effectiveness in many applications. However, their mathematical properties are generally not well understood. If there is…
Why Deep Surgical Models Fail?: Revisiting Surgical Action Triplet Recognition through the Lens of Robustness
Yanqi Cheng, Lihao Liu, Shujun Wang +3
Surgical action triplet recognition provides a better understanding of the surgical scene. This task is of high relevance as it provides the surgeon with context-aware support and…
Self-Supervised Learning of Phenotypic Representations from Cell Images with Weak Labels
Jan Oscar Cross-Zamirski, Guy Williams, Elizabeth Mouchet +3
We propose WS-DINO as a novel framework to use weak label information in learning phenotypic representations from high-content fluorescent images of cells. Our model is based on a…
Spectral decomposition of atomic structures in heterogeneous cryo-EM
Carlos Esteve-Yagüe, Willem Diepeveen, Ozan Öktem +1
We consider the problem of recovering the three-dimensional atomic structure of a flexible macromolecule from a heterogeneous cryo-EM dataset. The dataset contains noisy tomographi…
Regularising orientation estimation in Cryo-EM 3D map refinement through measure-based lifting over Riemannian manifolds
Willem Diepeveen, Jan Lellmann, Ozan Öktem +1
Motivated by the trade-off between noise-robustness and data-consistency for joint 3D map reconstruction and rotation estimation in single particle cryogenic-electron microscopy (C…
Practical Operator Sketching Framework for Accelerating Iterative Data-Driven Solutions in Inverse Problems
Junqi Tang, Guixian Xu, Subhadip Mukherjee +1
We propose a new operator-sketching paradigm for designing efficient iterative data-driven reconstruction (IDR) schemes, e.g. Plug-and-Play algorithms and deep unrolling networks.…
Joint reconstruction-segmentation on graphs
Jeremy Budd, Yves van Gennip, Jonas Latz +2
Practical image segmentation tasks concern images which must be reconstructed from noisy, distorted, and/or incomplete observations. A recent approach for solving such tasks is to…
Stochastic Primal-Dual Three Operator Splitting Algorithm with Extension to Equivariant Regularization-by-Denoising
Junqi Tang, Matthias Ehrhardt, Carola-Bibiane Schönlieb
In this work we propose a stochastic primal-dual three-operator splitting algorithm (TOS-SPDHG) for solving a class of convex three-composite optimization problems. Our proposed sc…
Non-Uniform Diffusion Models
Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb +1
Diffusion models have emerged as one of the most promising frameworks for deep generative modeling. In this work, we explore the potential of non-uniform diffusion models. We show…
Data-Driven Mirror Descent with Input-Convex Neural Networks
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang +1
Learning-to-optimize is an emerging framework that seeks to speed up the solution of certain optimization problems by leveraging training data. Learned optimization solvers have be…
Learned reconstruction methods with convergence guarantees
Subhadip Mukherjee, Andreas Hauptmann, Ozan Öktem +2
In recent years, deep learning has achieved remarkable empirical success for image reconstruction. This has catalyzed an ongoing quest for precise characterization of correctness a…
Unsupervised Learning of the Total Variation Flow
Tamara G. Grossmann, Sören Dittmer, Yury Korolev +1
The total variation (TV) flow generates a scale-space representation of an image based on the TV functional. This gradient flow observes desirable features for images, such as shar…
Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting
Lihao Liu, Chenyang Hong, Angelica I. Aviles-Rivero +1
We address the problem of automated nuclear segmentation, classification, and quantification from Haematoxylin and Eosin stained histology images, which is of great relevance for s…
Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification
Angelica I. Aviles-Rivero, Christina Runkel, Nicolas Papadakis +2
The automatic early diagnosis of prodromal stages of Alzheimer's disease is of great relevance for patient treatment to improve quality of life. We address this problem as a multi-…
Multi-modal learning for predicting the genotype of glioma
Yiran Wei, Xi Chen, Lei Zhu +4
The isocitrate dehydrogenase (IDH) gene mutation is an essential biomarker for the diagnosis and prognosis of glioma. It is promising to better predict glioma genotype by integrati…
Unsupervised Clustering of Roman Potsherds via Variational Autoencoders
Simone Parisotto, Ninetta Leone, Carola-Bibiane Schönlieb +1
In this paper we propose an artificial intelligence imaging solution to support archaeologists in the classification task of Roman commonware potsherds. Usually, each potsherd is r…