Christoph Brune

DeepAI AI Chat

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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...

0 Elina Thibeau-Sutre, et al. ∙

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...

0 Shunxin Wang, et al. ∙

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 ...

0 Ioana Mazilu, et al. ∙

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...

0 Shunxin Wang, et al. ∙

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...

0 Nicolò Botteghi, et al. ∙

research

∙ 05/25/2023

Embeddings between Barron spaces with higher order activation functions

The approximation properties of infinitely wide shallow neural networks ...

0 Tjeerd Jan Heeringa, et al. ∙

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...

6 Sven Dummer, et al. ∙

research

∙ 03/02/2023

Implicit Neural Representations for Modeling of Abdominal Aortic Aneurysm Progression

Abdominal aortic aneurysms (AAAs) are progressive dilatations of the abd...

21 Dieuwertje Alblas, et al. ∙

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...

5 Julian Suk, et al. ∙

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...

0 Yannik Wotte, et al. ∙

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...

23 Julian Suk, et al. ∙

research

∙ 11/09/2022

Duality for Neural Networks through Reproducing Kernel Banach Spaces

Reproducing Kernel Hilbert spaces (RKHS) have been a very successful too...

8 Len Spek, et al. ∙

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 ...

16 Nicolò Botteghi, et al. ∙

research

∙ 08/27/2022

Unsupervised Representation Learning in Deep Reinforcement Learning: A Review

This review addresses the problem of learning abstract representations o...

6 Nicolò Botteghi, et al. ∙

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...

20 Dieuwertje Alblas, et al. ∙

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...

4 Nathan Blanken, et al. ∙

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...

19 Dieuwertje Alblas, et al. ∙

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, ...

23 Julian Suk, et al. ∙

research

∙ 07/08/2021

Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning

Inspection and maintenance are two crucial aspects of industrial pipelin...

11 Nicolò Botteghi, et al. ∙

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...

5 Nicolò Botteghi, et al. ∙

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...

11 Nicolò Botteghi, et al. ∙

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...

16 Manu Kalia, et al. ∙

research

∙ 07/29/2020

Low Dimensional State Representation Learning with Reward-shaped Priors

Reinforcement Learning has been able to solve many complicated robotics ...

4 Nicolò Botteghi, et al. ∙

research

∙ 12/20/2019

Learned SVD: solving inverse problems via hybrid autoencoding

Our world is full of physics-driven data where effective mappings betwee...

0 Yoeri E. Boink, et al. ∙

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...

0 Leonie Zeune, et al. ∙

research

∙ 04/22/2016

Multiscale Segmentation via Bregman Distances and Nonlinear Spectral Analysis

In biomedical imaging reliable segmentation of objects (e.g. from small ...

0 Leonie Zeune, et al. ∙

research

∙ 07/26/2012

Optimal Data Collection For Informative Rankings Expose Well-Connected Graphs

Given a graph where vertices represent alternatives and arcs represent p...

0 Braxton Osting, et al. ∙

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