297 citations · 1.5k across the 199 of their papers we have counts for
40 papers · 1 filter
Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations
Yi Zhang, Chun-Wun Cheng, Junyi He +4
We introduce SONO, a novel method leveraging Second-Order Neural Ordinary Differential Equations (Second-Order NODEs) to enhance cross-modal few-shot learning. By employing a simpl…
Stochastic Multiresolution Image Sketching for Inverse Imaging Problems
Alessandro Perelli, Carola-Bibiane Schonlieb, Matthias J. Ehrhardt
A challenge in high-dimensional inverse problems is developing iterative solvers to find the accurate solution of regularized optimization problems with low computational cost. An…
Training Data Reconstruction: Privacy due to Uncertainty?
Christina Runkel, Kanchana Vaishnavi Gandikota, Jonas Geiping +2
Being able to reconstruct training data from the parameters of a neural network is a major privacy concern. Previous works have shown that reconstructing training data, under certa…
Benchmarking learned algorithms for computed tomography image reconstruction tasks
Maximilian B. Kiss, Ander Biguri, Zakhar Shumaylov +4
Computed tomography (CT) is a widely used non-invasive diagnostic method in various fields, and recent advances in deep learning have led to significant progress in CT image recons…
Symplectic Neural Flows for Modeling and Discovery
Priscilla Canizares, Davide Murari, Carola-Bibiane Schönlieb +2
Hamilton's equations are fundamental for modeling complex physical systems, where preserving key properties such as energy and momentum is crucial for reliable long-term simulation…
Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings
Billy Joe Franks, Moshe Eliasof, Semih Cantürk +4
Recent advances in integrating positional and structural encodings (PSEs) into graph neural networks (GNNs) have significantly enhanced their performance across various graph learn…
On the Utilization of Unique Node Identifiers in Graph Neural Networks
Maya Bechler-Speicher, Moshe Eliasof, Carola-Bibiane Schönlieb +2
Graph Neural Networks have inherent representational limitations due to their message-passing structure. Recent work has suggested that these limitations can be overcome by using u…
Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space
Yangming Li, Chieh-Hsin Lai, Carola-Bibiane Schönlieb +2
Deep Generative Models (DGMs), including Energy-Based Models (EBMs) and Score-based Generative Models (SGMs), have advanced high-fidelity data generation and complex continuous dis…
Parameter choices in HaarPSI for IQA with medical images
Clemens Karner, Janek Gröhl, Ian Selby +11
When developing machine learning models, image quality assessment (IQA) measures are a crucial component for the evaluation of obtained output images. However, commonly used full-r…
Pullback Flow Matching on Data Manifolds
Friso de Kruiff, Erik Bekkers, Ozan Öktem +2
We propose Pullback Flow Matching (PFM), a novel framework for generative modeling on data manifolds. Unlike existing methods that assume or learn restrictive closed-form manifold…
Mamba Neural Operator: Who Wins? Transformers vs. State-Space Models for PDEs
Chun-Wun Cheng, Jiahao Huang, Yi Zhang +3
Partial differential equations (PDEs) are widely used to model complex physical systems, but solving them efficiently remains a significant challenge. Recently, Transformers have e…
Celcomen: spatial causal disentanglement for single-cell and tissue perturbation modeling
Stathis Megas, Daniel G. Chen, Krzysztof Polanski +3
Celcomen leverages a mathematical causality framework to disentangle intra- and inter- cellular gene regulation programs in spatial transcriptomics and single-cell data through a g…
Nested Bregman Iterations for Decomposition Problems
Tobias Wolf, Derek Driggs, Kostas Papafitsoros +2
We consider the task of image reconstruction while simultaneously decomposing the reconstructed image into components with different features. A commonly used tool for this is a va…
Learned denoising with simulated and experimental low-dose CT data
Maximilian B. Kiss, Ander Biguri, Carola-Bibiane Schönlieb +2
Like in many other research fields, recent developments in computational imaging have focused on developing machine learning (ML) approaches to tackle its main challenges. To impro…
Learning Regularization for Graph Inverse Problems
Moshe Eliasof, Md Shahriar Rahim Siddiqui, Carola-Bibiane Schönlieb +1
In recent years, Graph Neural Networks (GNNs) have been utilized for various applications ranging from drug discovery to network design and social networks. In many applications, i…
Deep Generative Classification of Blood Cell Morphology
Simon Deltadahl, Julian Gilbey, Christine Van Laer +16
Accurate classification of haematological cells is critical for diagnosing blood disorders, but presents significant challenges for machine automation owing to the complexity of ce…
Blessing of Dimensionality for Approximating Sobolev Classes on Manifolds
Hong Ye Tan, Subhadip Mukherjee, Junqi Tang +1
The manifold hypothesis says that natural high-dimensional data lie on or around a low-dimensional manifold. The recent success of statistical and learning-based methods in very hi…
Contrastive Learning with Adaptive Neighborhoods for Brain Age Prediction on 3D Stiffness Maps
Jakob Träuble, Lucy Hiscox, Curtis Johnson +3
In the field of neuroimaging, accurate brain age prediction is pivotal for uncovering the complexities of brain aging and pinpointing early indicators of neurodegenerative conditio…
Artificial Immunofluorescence in a Flash: Rapid Synthetic Imaging from Brightfield Through Residual Diffusion
Xiaodan Xing, Chunling Tang, Siofra Murdoch +10
Immunofluorescent (IF) imaging is crucial for visualizing biomarker expressions, cell morphology and assessing the effects of drug treatments on sub-cellular components. IF imaging…
NODE-Adapter: Neural Ordinary Differential Equations for Better Vision-Language Reasoning
Yi Zhang, Chun-Wun Cheng, Ke Yu +3
In this paper, we consider the problem of prototype-based vision-language reasoning problem. We observe that existing methods encounter three major challenges: 1) escalating resour…
Neural varifolds: an aggregate representation for quantifying the geometry of point clouds
Juheon Lee, Xiaohao Cai, Carola-Bibian Schönlieb +1
Point clouds are popular 3D representations for real-life objects (such as in LiDAR and Kinect) due to their detailed and compact representation of surface-based geometry. Recent a…
G-Adaptivity: optimised graph-based mesh relocation for finite element methods
James Rowbottom, Georg Maierhofer, Teo Deveney +6
We present a novel, and effective, approach to achieve optimal mesh relocation in finite element methods (FEMs). The cost and accuracy of FEMs is critically dependent on the choice…
DiGRAF: Diffeomorphic Graph-Adaptive Activation Function
Krishna Sri Ipsit Mantri, Xinzhi Wang, Carola-Bibiane Schönlieb +3
In this paper, we propose a novel activation function tailored specifically for graph data in Graph Neural Networks (GNNs). Motivated by the need for graph-adaptive and flexible ac…
Spatiotemporal Graph Neural Network Modelling Perfusion MRI
Ruodan Yan, Carola-Bibiane Schönlieb, Chao Li
Perfusion MRI (pMRI) offers valuable insights into tumor vascularity and promises to predict tumor genotypes, thus benefiting prognosis for glioma patients, yet effective models ta…