297 citations · 1.5k across the 199 of their papers we have counts for
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Training-Free Dual Hyperbolic Adapters for Better Cross-Modal Reasoning
Yi Zhang, Chun-Wun Cheng, Junyi He +5
Recent research in Vision-Language Models (VLMs) has significantly advanced our capabilities in cross-modal reasoning. However, existing methods suffer from performance degradation…
HypeR Adaptivity: Joint -Adaptive Meshing via Hypergraph Multi-Agent Deep Reinforcement Learning
Niccolò Grillo, James Rowbottom, Pietro Liò +2
Adaptive mesh refinement is central to the efficient solution of partial differential equations (PDEs) via the finite element method (FEM). Classical -adaptivity optimizes verte…
Laplace Learning in Wasserstein Space
Mary Chriselda Antony Oliver, Michael Roberts, Carola-Bibiane Schönlieb +1
The manifold hypothesis posits that high-dimensional data typically resides on low-dimensional sub spaces. In this paper, we assume manifold hypothesis to investigate graph-based s…
When is a System Discoverable from Data? Discovery Requires Chaos
Zakhar Shumaylov, Peter Zaika, Philipp Scholl +3
The deep learning revolution has enabled major AI advances in physical sciences, particularly in discovering dynamical systems from observational data. Yet the reliability of learn…
Graph Neural Regularizers for PDE Inverse Problems
William Lauga, James Rowbottom, Alexander Denker +3
We present a framework for solving a broad class of ill-posed inverse problems governed by partial differential equations (PDEs), where the target coefficients of the forward opera…
Learning Regularization Functionals for Inverse Problems: A Comparative Study
Johannes Hertrich, Hok Shing Wong, Alexander Denker +16
In recent years, a variety of learned regularization frameworks for solving inverse problems in imaging have emerged. These offer flexible modeling together with mathematical insig…
GeoSplat: A Deep Dive into Geometry-Constrained Gaussian Splatting
Yangming Li, Chaoyu Liu, Lihao Liu +2
A few recent works explored incorporating geometric priors to regularize the optimization of Gaussian splatting, further improving its performance. However, those early studies mai…
LamiGauss: Pitching Radiative Gaussian for Sparse-View X-ray Laminography Reconstruction
Chu Chen, Ander Biguri, Jean-Michel Morel +3
X-ray Computed Laminography (CL) is essential for non-destructive inspection of plate-like structures in applications such as microchips and composite battery materials, where trad…
Neural Fields for Highly Accelerated 2D Cine Phase Contrast MRI
Pablo Arratia, Martin J. Graves, Mary McLean +5
2D cine phase contrast (CPC) MRI provides quantitative information on blood velocity and flow within the human vasculature. However, data acquisition is time-consuming, motivating…
Vehicle detection from GSV imagery: Predicting travel behaviour for cycling and motorcycling using Computer Vision
Kyriaki, Kokka, Rahul Goel +7
Transportation influence health by shaping exposure to physical activity, air pollution and injury risk. Comparative data on cycling and motorcycling behaviours is scarce, particul…
Deep Network Trainability via Persistent Subspace Orthogonality
Alex Massucco, Davide Murari, Carola-Bibiane Schönlieb
Training neural networks via backpropagation is often hindered by vanishing or exploding gradients. In this work, we design architectures that mitigate these issues by analyzing an…
Data-driven approaches to inverse problems
Carola-Bibiane Schönlieb, Zakhar Shumaylov
Inverse problems are concerned with the reconstruction of unknown physical quantities using indirect measurements and are fundamental across diverse fields such as medical imaging,…
Adaptive Correction for Ensuring Conservation Laws in Neural Operators
Chaoyu Liu, Yangming Li, Zhongying Deng +2
Physical laws, such as the conversation of mass and momentum, are fundamental principles in many physical systems. Neural operators have achieved promising performance in learning…
Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling
Andrea Ceni, Alessio Gravina, Claudio Gallicchio +3
The recent success of State-Space Models (SSMs) in sequence modeling has motivated their adaptation to graph learning, giving rise to Graph State-Space Models (GSSMs). However, exi…
Approximation theory for 1-Lipschitz ResNets
Davide Murari, Takashi Furuya, Carola-Bibiane Schönlieb
1-Lipschitz neural networks are fundamental for generative modelling, inverse problems, and robust classifiers. In this paper, we focus on 1-Lipschitz residual networks (ResNets) b…
Brain Foundation Models with Hypergraph Dynamic Adapter for Brain Disease Analysis
Zhongying Deng, Haoyu Wang, Ziyan Huang +6
Brain diseases, such as Alzheimer's disease and brain tumors, present profound challenges due to their complexity and societal impact. Recent advancements in brain foundation model…
FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks
Or Feldman, Krishna Sri Ipsit Mantri, Carola-Bibiane Schönlieb +2
Aggregating temporal signals from historic interactions is a key step in future link prediction on dynamic graphs. However, incorporating long histories is resource-intensive. Henc…
Implicit U-KAN2.0: Dynamic, Efficient and Interpretable Medical Image Segmentation
Chun-Wun Cheng, Yining Zhao, Yanqi Cheng +3
Image segmentation is a fundamental task in both image analysis and medical applications. State-of-the-art methods predominantly rely on encoder-decoder architectures with a U-shap…
Inverse Evolution Data Augmentation for Neural PDE Solvers
Chaoyu Liu, Chris Budd, Carola-Bibiane Schönlieb
Neural networks have emerged as promising tools for solving partial differential equations (PDEs), particularly through the application of neural operators. Training neural operato…
Generative Unordered Flow for Set-Structured Data Generation
Yangming Li, Chaoyu Liu, Carola-Bibiane Schönlieb
Flow-based generative models have demonstrated promising performance across a broad spectrum of data modalities (e.g., image and text). However, there are few works exploring their…