Thilo Strauss
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View article: MCMC-Net: accelerating Markov Chain Monte Carlo with neural networks for inverse problems
MCMC-Net: accelerating Markov Chain Monte Carlo with neural networks for inverse problems Open
In many computational problems, using the Markov Chain Monte Carlo (MCMC) can be prohibitively time-consuming. We propose MCMC-Net, a simple yet efficient way to accelerate MCMC via neural networks. The key idea of our approach is to subst…
View article: Preference-Based Gradient Estimation for ML-Guided Approximate Combinatorial Optimization
Preference-Based Gradient Estimation for ML-Guided Approximate Combinatorial Optimization Open
Combinatorial optimization (CO) problems arise across a broad spectrum of domains, including medicine, logistics, and manufacturing. While exact solutions are often computationally infeasible, many practical applications require high-quali…
View article: MCMC-Net: Accelerating Markov Chain Monte Carlo with Neural Networks for Inverse Problems
MCMC-Net: Accelerating Markov Chain Monte Carlo with Neural Networks for Inverse Problems Open
In many computational problems, using the Markov Chain Monte Carlo (MCMC) can be prohibitively time-consuming. We propose MCMC-Net, a simple yet efficient way to accelerate MCMC via neural networks. The key idea of our approach is to subst…
View article: A DeepONet for inverting the Neumann-to-Dirichlet Operator in Electrical Impedance Tomography: An approximation theoretic perspective and numerical results
A DeepONet for inverting the Neumann-to-Dirichlet Operator in Electrical Impedance Tomography: An approximation theoretic perspective and numerical results Open
In this work, we consider the non-invasive medical imaging modality of Electrical Impedance Tomography, where the problem is to recover the conductivity in a medium from a set of data that arises out of a current-to-voltage map (Neumann-to…
View article: Simultaneous Estimation of Piecewise Constant Coefficients in Elliptic PDEs via Bayesian Level-Set Methods
Simultaneous Estimation of Piecewise Constant Coefficients in Elliptic PDEs via Bayesian Level-Set Methods Open
In this article, we propose a non-parametric Bayesian level-set method for simultaneous reconstruction of two different piecewise constant coefficients in an elliptic partial differential equation. We show that the Bayesian formulation of …
View article: Differentiable Optimization for Orchestration: Resource Offloading for Vehicles in Smart Cities
Differentiable Optimization for Orchestration: Resource Offloading for Vehicles in Smart Cities Open
Connected and Autonomous Vehicles (CAV) which interact with Roadside Units (RSU) as part of a smart city infrastructure are currently seeing first real-world deployments. Not only can CAVs benefit from access to a cities’ infrastructure by…
View article: Comparison of Different Radial Basis Function Networks for the Electrical Impedance Tomography (EIT) Inverse Problem
Comparison of Different Radial Basis Function Networks for the Electrical Impedance Tomography (EIT) Inverse Problem Open
This paper aims to determine whether regularization improves image reconstruction in electrical impedance tomography (EIT) using a radial basis network. The primary purpose is to investigate the effect of regularization to estimate the net…
View article: Implicit Solutions of the Electrical Impedance Tomography Inverse Problem in the Continuous Domain with Deep Neural Networks
Implicit Solutions of the Electrical Impedance Tomography Inverse Problem in the Continuous Domain with Deep Neural Networks Open
Electrical impedance tomography (EIT) is a non-invasive imaging modality used for estimating the conductivity of an object Ω from boundary electrode measurements. In recent years, researchers achieved substantial progress in analytical and…
View article: Learning Implicit Surface Light Fields
Learning Implicit Surface Light Fields Open
Implicit representations of 3D objects have recently achieved impressive results on learning-based 3D reconstruction tasks. While existing works use simple texture models to represent object appearance, photo-realistic image synthesis requ…
View article: Statistical Inversion Using Sparsity and Total Variation Prior And Monte Carlo Sampling Method For Diffuse Optical Tomography
Statistical Inversion Using Sparsity and Total Variation Prior And Monte Carlo Sampling Method For Diffuse Optical Tomography Open
In this paper, we formulate the reconstruction problem in diffuse optical tomography (DOT) in a statistical setting for determining the optical parameters, scattering and absorption, from boundary photon density measurements. A special kin…
View article: CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data
CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data Open
We propose a novel neural network architecture for detecting intrusions on the CAN bus. The Controller Area Network (CAN) is the standard communication method between the Electronic Control Units (ECUs) of automobiles. However, CAN lacks s…
View article: Texture Fields: Learning Texture Representations in Function Space
Texture Fields: Learning Texture Representations in Function Space Open
In recent years, substantial progress has been achieved in learning-based reconstruction of 3D objects. At the same time, generative models were proposed that can generate highly realistic images. However, despite this success in these clo…
View article: Unpaired High-Resolution and Scalable Style Transfer Using Generative\n Adversarial Networks
Unpaired High-Resolution and Scalable Style Transfer Using Generative\n Adversarial Networks Open
Neural networks have proven their capabilities by outperforming many other\napproaches on regression or classification tasks on various kinds of data.\nOther astonishing results have been achieved using neural nets as data\ngenerators, esp…
View article: Unpaired High-Resolution and Scalable Style Transfer Using Generative Adversarial Networks
Unpaired High-Resolution and Scalable Style Transfer Using Generative Adversarial Networks Open
Neural networks have proven their capabilities by outperforming many other approaches on regression or classification tasks on various kinds of data. Other astonishing results have been achieved using neural nets as data generators, especi…
View article: Alternatives for Generating a Reduced Basis to Solve the Hyperspectral Diffuse Optical Tomography Model
Alternatives for Generating a Reduced Basis to Solve the Hyperspectral Diffuse Optical Tomography Model Open
The Reduced Basis Method (RBM) is a model reduction technique used to solve parametric PDEs that relies upon a basis set of solutions to the PDE at specific parameter values. To generate this reduced basis, the set of a small number of par…
View article: Ensemble Methods as a Defense to Adversarial Perturbations Against Deep\n Neural Networks
Ensemble Methods as a Defense to Adversarial Perturbations Against Deep\n Neural Networks Open
Deep learning has become the state of the art approach in many machine\nlearning problems such as classification. It has recently been shown that deep\nlearning is highly vulnerable to adversarial perturbations. Taking the camera\nsystems …
View article: Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks
Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks Open
Deep learning has become the state of the art approach in many machine learning problems such as classification. It has recently been shown that deep learning is highly vulnerable to adversarial perturbations. Taking the camera systems of …
View article: Statistical Inversion of Absolute Permeability in Single-phase Darcy Flow
Statistical Inversion of Absolute Permeability in Single-phase Darcy Flow Open
In this paper, we formulate the permeability inverse problem in the Bayesian framework using total variation (TV) and fp (0 < p δ 2) regularization prior. We use the Markov Chain Monte Carlo (MCMC) method for sampling the posterior distrib…