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Found 26 papers, 13 papers with code
Towards Learned Emulation of Interannual Water Isotopologue Variations in General Circulation Models
Simulating abundances of stable water isotopologues, i. e. molecules differing in their isotopic composition, within climate models allows for comparisons with proxy data and, thus, for testing hypotheses about past climate and validating climate models under varying climatic conditions.
Neural Superstatistics: A Bayesian Method for Estimating Dynamic Models of Cognition
In its simplest form, such a model entails a hierarchy between a low-level observation model and a high-level transition model.
Whitening Convergence Rate of Coupling-based Normalizing Flows
For the first time, we make a quantitative statement about this kind of convergence: We prove that all coupling-based normalizing flows perform whitening of the data distribution (i. e. diagonalize the covariance matrix) and derive corresponding convergence bounds that show a linear convergence rate in the depth of the flow.
Anomaly Detection using Contrastive Normalizing Flows
We use a self-supervised feature extractor trained on the auxiliary dataset and train a normalizing flow on the extracted features by maximizing the likelihood on in-distribution data and minimizing the likelihood on the auxiliary dataset.
Content-Aware Differential Privacy with Conditional Invertible Neural Networks
Manipulation of the latent space leads to a modified image while preserving important details.
Towards Multimodal Depth Estimation from Light Fields
We argue that this is due current methods only considering a single "true" depth, even when multiple objects at different depths contributed to the color of a single pixel.
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks
Neural density estimators have proven remarkably powerful in performing efficient simulation-based Bayesian inference in various research domains.
Conditional Invertible Neural Networks for Diverse Image-to-Image Translation
We introduce a new architecture called a conditional invertible neural network (cINN), and use it to address the task of diverse image-to-image translation for natural images.
Benchmarking Invertible Architectures on Inverse Problems
Recent work demonstrated that flow-based invertible neural networks are promising tools for solving ambiguous inverse problems.
Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging
Multispectral photoacoustic imaging (PAI) is an emerging imaging modality which enables the recovery of functional tissue parameters such as blood oxygenation.
Learning Robust Models Using The Principle of Independent Causal Mechanisms
Standard supervised learning breaks down under data distribution shift.
OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany
Mathematical models in epidemiology are an indispensable tool to determine the dynamics and important characteristics of infectious diseases.
Generative Classifiers as a Basis for Trustworthy Image Classification
Generative classifiers (GCs) are a promising class of models that are said to naturally accomplish these qualities.
Amortized Bayesian model comparison with evidential deep learning
This makes the method particularly effective in scenarios where model fit needs to be assessed for a large number of datasets, so that per-dataset inference is practically infeasible. Finally, we propose a novel way to measure epistemic uncertainty in model comparison problems.
BayesFlow: Learning complex stochastic models with invertible neural networks
In addition, our method incorporates a summary network trained to embed the observed data into maximally informative summary statistics.
Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification
In this work, firstly, we develop the theory and methodology of IB-INNs, a class of conditional normalizing flows where INNs are trained using the IB objective: Introducing a small amount of {\em controlled} information loss allows for an asymptotically exact formulation of the IB, while keeping the INN's generative capabilities intact.
Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)
Furthermore, the recovered informative latent variables will be in one-to-one correspondence with the true latent variables of the generating process, up to a trivial component-wise transformation.
Out of distribution detection for intra-operative functional imaging
Multispectral optical imaging is becoming a key tool in the operating room.
Conditional Invertible Neural Networks for Guided Image Generation
In this work, we address the task of natural image generation guided by a conditioning input.
Object Segmentation using Pixel-wise Adversarial Loss
Recent deep learning based approaches have shown remarkable success on object segmentation tasks.
Guided Image Generation with Conditional Invertible Neural Networks
We demonstrate these properties for the tasks of MNIST digit generation and image colorization.
HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference
Many recent invertible neural architectures are based on coupling block designs where variables are divided in two subsets which serve as inputs of an easily invertible (usually affine) triangular transformation.
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning
Unlike seeded watershed, the algorithm can accommodate not only attractive but also repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold.
Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks
Assessment of the specific hardware used in conjunction with such algorithms, however, has not properly addressed the possibility that the problem may be ill-posed.
Analyzing Inverse Problems with Invertible Neural Networks
Often, the forward process from parameter- to measurement-space is a well-defined function, whereas the inverse problem is ambiguous: one measurement may map to multiple different sets of parameters.
Learned Watershed: End-to-End Learning of Seeded Segmentation
Learned boundary maps are known to outperform hand- crafted ones as a basis for the watershed algorithm.
