research
∙
08/18/2023
Uncertainty-based quality assurance of carotid artery wall segmentation in black-blood MRI
The application of deep learning models to large-scale data sets require...
share
research
∙
08/12/2023
DFM-X: Augmentation by Leveraging Prior Knowledge of Shortcut Learning
Neural networks are prone to learn easy solutions from superficial stati...
research
∙
07/28/2023
Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space
Though modern microscopes have an autofocusing system to ensure optimal ...
research
∙
07/19/2023
What do neural networks learn in image classification? A frequency shortcut perspective
Frequency analysis is useful for understanding the mechanisms of represe...
research
∙
06/27/2023
Trajectory Generation, Control, and Safety with Denoising Diffusion Probabilistic Models
We present a framework for safety-critical optimal control of physical s...
research
∙
05/25/2023
Embeddings between Barron spaces with higher order activation functions
The approximation properties of infinitely wide shallow neural networks ...
research
∙
05/22/2023
RSA-INR: Riemannian Shape Autoencoding via 4D Implicit Neural Representations
Shape encoding and shape analysis are valuable tools for comparing shape...
research
∙
03/02/2023
Implicit Neural Representations for Modeling of Abdominal Aortic Aneurysm Progression
Abdominal aortic aneurysms (AAAs) are progressive dilatations of the abd...
research
∙
02/17/2023
SE(3) symmetry lets graph neural networks learn arterial velocity estimation from small datasets
Hemodynamic velocity fields in coronary arteries could be the basis of v...
research
∙
12/29/2022
Discovering Efficient Periodic Behaviours in Mechanical Systems via Neural Approximators
It is well known that conservative mechanical systems exhibit local osci...
research
∙
12/09/2022
Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall
Computational fluid dynamics (CFD) is a valuable asset for patient-speci...
research
∙
11/09/2022
Duality for Neural Networks through Reproducing Kernel Banach Spaces
Reproducing Kernel Hilbert spaces (RKHS) have been a very successful too...
research
∙
08/27/2022
Deep Kernel Learning of Dynamical Models from High-Dimensional Noisy Data
This work proposes a Stochastic Variational Deep Kernel Learning method ...
research
∙
08/27/2022
Unsupervised Representation Learning in Deep Reinforcement Learning: A Review
This review addresses the problem of learning abstract representations o...
research
∙
07/29/2022
Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modeling
Personalised 3D vascular models are valuable for diagnosis, prognosis an...
research
∙
04/09/2022
Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning
Recently, super-resolution ultrasound imaging with ultrasound localizati...
research
∙
12/02/2021
Deep Learning-Based Carotid Artery Vessel Wall Segmentation in Black-Blood MRI Using Anatomical Priors
Carotid artery vessel wall thickness measurement is an essential step in...
research
∙
09/10/2021
Mesh convolutional neural networks for wall shear stress estimation in 3D artery models
Computational fluid dynamics (CFD) is a valuable tool for personalised, ...
research
∙
07/08/2021
Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning
Inspection and maintenance are two crucial aspects of industrial pipelin...
research
∙
07/04/2021
Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics
Deep Reinforcement Learning has shown its ability in solving complicated...
research
∙
07/04/2021
Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces
Autonomous robots require high degrees of cognitive and motoric intellig...
research
∙
06/09/2021
Learning normal form autoencoders for data-driven discovery of universal,parameter-dependent governing equations
Complex systems manifest a small number of instabilities and bifurcation...
research
∙
07/29/2020
Low Dimensional State Representation Learning with Reward-shaped Priors
Reinforcement Learning has been able to solve many complicated robotics ...
research
∙
12/20/2019
Learned SVD: solving inverse problems via hybrid autoencoding
Our world is full of physics-driven data where effective mappings betwee...
research
∙
03/16/2017
Combining Contrast Invariant L1 Data Fidelities with Nonlinear Spectral Image Decomposition
This paper focuses on multi-scale approaches for variational methods and...
research
∙
04/22/2016
Multiscale Segmentation via Bregman Distances and Nonlinear Spectral Analysis
In biomedical imaging reliable segmentation of objects (e.g. from small ...
research
∙
07/26/2012