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09/18/2023
Application-driven Validation of Posteriors in Inverse Problems
Current deep learning-based solutions for image analysis tasks are commo...
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08/04/2023
A Review of Change of Variable Formulas for Generative Modeling
Change-of-variables (CoV) formulas allow to reduce complicated probabili...
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06/28/2023
BayesFlow: Amortized Bayesian Workflows With Neural Networks
Modern Bayesian inference involves a mixture of computational techniques...
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06/23/2023
On the Convergence Rate of Gaussianization with Random Rotations
Gaussianization is a simple generative model that can be trained without...
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06/02/2023
Maximum Likelihood Training of Autoencoders
Maximum likelihood training has favorable statistical properties and is ...
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03/20/2023
Training Invertible Neural Networks as Autoencoders
Autoencoders are able to learn useful data representations in an unsuper...
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03/17/2023
Unsupervised Domain Transfer with Conditional Invertible Neural Networks
Synthetic medical image generation has evolved as a key technique for ne...
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03/17/2023
Finding Competence Regions in Domain Generalization
We propose a "learning to reject" framework to address the problem of si...
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02/17/2023
JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models
This work proposes ”jointly amortized neural approximation” (JANA) of in...
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01/31/2023
Towards Learned Emulation of Interannual Water Isotopologue Variations in General Circulation Models
Simulating abundances of stable water isotopologues, i.e. molecules diff...
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11/23/2022
Neural Superstatistics: A Bayesian Method for Estimating Dynamic Models of Cognition
Mathematical models of cognition are often memoryless and ignore potenti...
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10/25/2022
Whitening Convergence Rate of Coupling-based Normalizing Flows
Coupling-based normalizing flows (e.g. RealNVP) are a popular family of ...
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08/30/2022
Anomaly Detection using Contrastive Normalizing Flows
Detecting test data deviating from training data is a central problem fo...
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07/29/2022
Content-Aware Differential Privacy with Conditional Invertible Neural Networks
Differential privacy (DP) has arisen as the gold standard in protecting ...
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03/30/2022
Towards Multimodal Depth Estimation from Light Fields
Light field applications, especially light field rendering and depth est...
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12/16/2021
BayesFlow can reliably detect Model Misspecification and Posterior Errors in Amortized Bayesian Inference
Neural density estimators have proven remarkably powerful in performing ...
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05/05/2021
Conditional Invertible Neural Networks for Diverse Image-to-Image Translation
We introduce a new architecture called a conditional invertible neural n...
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01/26/2021
Benchmarking Invertible Architectures on Inverse Problems
Recent work demonstrated that flow-based invertible neural networks are ...
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11/10/2020
Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging
Multispectral photoacoustic imaging (PAI) is an emerging imaging modalit...
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10/14/2020
Learning Robust Models Using The Principle of Independent Causal Mechanisms
Standard supervised learning breaks down under data distribution shift. ...
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10/01/2020
Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks
Mathematical models in epidemiology strive to describe the dynamics and ...
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07/29/2020
Generative Classifiers as a Basis for Trustworthy Computer Vision
With the maturing of deep learning systems, trustworthiness is becoming ...
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04/22/2020
Amortized Bayesian model comparison with evidential deep learning
Comparing competing mathematical models of complex natural processes is ...
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03/13/2020
BayesFlow: Learning complex stochastic models with invertible neural networks
Estimating the parameters of mathematical models is a common problem in ...
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01/17/2020
Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling
The Information Bottleneck (IB) principle offers a unified approach to m...
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01/14/2020
Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)
A central question of representation learning asks under which condition...
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11/05/2019
Out of distribution detection for intra-operative functional imaging
Multispectral optical imaging is becoming a key tool in the operating ro...
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09/23/2019
Object Segmentation using Pixel-wise Adversarial Loss
Recent deep learning based approaches have shown remarkable success on o...
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07/04/2019
Guided Image Generation with Conditional Invertible Neural Networks
In this work, we address the task of natural image generation guided by ...
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05/25/2019
HINT: Hierarchical Invertible Neural Transport for General and Sequential Bayesian inference
In this paper, we introduce Hierarchical Invertible Neural Transport (HI...
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04/25/2019
The Mutex Watershed and its Objective: Efficient, Parameter-Free Image Partitioning
Image partitioning, or segmentation without semantics, is the task of de...
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03/08/2019
Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks
Purpose: Optical imaging is evolving as a key technique for advanced sen...
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08/14/2018
Analyzing Inverse Problems with Invertible Neural Networks
In many tasks, in particular in natural science, the goal is to determin...
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04/07/2017