New model reconstructs flow from sparse data with uncertainty quantification.
problem Reconstructing nonlinear flow from limited observations.
method Semi-Conditional Variational Autoencoder (SCVAE) for probabilistic flow reconstruction.
result SCVAE improves reconstruction accuracy compared to Gappy Proper Orthogonal Decomposition (GPOD).
Proposes a new method for efficient model reconstruction with uncertain parameters.
problem Reconstructing models with latent variables or parameters of unknown distribution.
method Local squared Wasserstein-2 (W_2) method.
result Efficiently reconstructs output distributions from observation data.
This work tackles uncertainty quantification in tomography reconstruction.
problem Ill-posed nature of tomographic reconstruction leading to no unique solution.
method Gaussian process modeling to incorporate prior knowledge and experimental noises.
result Efficient uncertainty quantification in tomographic reconstruction.
Method estimates uncertainty in CT reconstructions.
problem Lack of accurate uncertainty estimates in deep-learning CT reconstructions.
method Linearised deep image prior with conjugate Gaussian-linear model error bars and Gaussian surrogate for TV regularisation.
result Method provides superior calibration of uncertainty estimates.
Bayesian framework learns prior from data to quantify uncertainty in MRI reconstruction.
problem Quantifying uncertainty in deep learning solutions for inverse problems.
method Adopting denoising score matching to learn prior from data, using it in an annealed Hamiltonian Monte-Carlo scheme.
result The approach yields high-quality reconstructions and assesses uncertainty on specific features.
The paper extends a neural model to estimate uncertainty in multi-interaction trajectory reconstruction.
problem Lack of uncertainty estimation in neural models for multi-interaction trajectory reconstruction.
method Extended Factorised Neural Relational Inference model to output mean and standard deviation for each component of the phase space vector, using various loss functions.
result Demonstrated the importance of physical meaning of variables and existence of local minima during training.
Generative models improve image reconstruction and uncertainty quantification.
problem Bayesian inverse problems, especially image reconstruction from noisy and incomplete data.
method Data-driven priors and computationally tractable posterior analysis.
result Efficient uncertainty quantification without retraining for different corruption types.
Paper improves MRI reconstruction by separating target labels and prediction error.
problem Improving MRI reconstruction accuracy by estimating prediction error.
method Proposes a novel method to estimate target labels and prediction error separately.
result Significantly better MRI reconstruction results achieved compared to state-of-the-art methods.
New method quantifies uncertainty in imaging problems.
problem Uncertainty quantification in imaging inverse problems.
method Equivariant bootstrapping based on parametric bootstrap algorithm.
result Delivers accurate high-dimensional confidence regions.
Normalizing flows improve ptychography reconstruction quality and uncertainty quantification.
problem Challenges in ptychography due to large-scale nonlinear and non-convex inverse problems and photon statistics.
method Use of normalizing flows to model the posterior distribution and quantify reconstruction uncertainty.
result Normalizing flows enable better characterization and uncertainty quantification in ptychography reconstructions.
Single linear solve combines surface reconstruction and uncertainty quantification.
problem Reconstructing surfaces from partial point clouds with uncertainty.
method Geometric Gaussian processes for stochastic surface reconstruction.
result Single linear solve for surface reconstruction with probabilistic capabilities.
New method samples from posterior distributions of network reconstructions.
problem Inferring unseen interactions in systems from observed dynamics.
method Efficient MCMC algorithm for sampling posterior distributions.
result Reveals full population of answers weighted by plausibility.
Single-cell gene expression data provide invaluable resources for systematic characterization of cellular hierarchy in multi-cellular organisms. However, cell lineage reconstruction is still often associated with significant uncertainty due to technological constraints. Such uncertainties have not been taken into accou…
Framework for confidence estimation in deep CT reconstructions.
problem Uncertainty in deep learning-based CT reconstructions.
method Sequential likelihood mixing framework with log-linear forward model.
result Deep models yield tighter confidence regions than classical methods.
Interval Neural Networks detect instabilities in image reconstructions.
problem Detecting instabilities in deep learning image reconstructions.
method Employed uncertainty quantification methods with Interval Neural Networks.
result Interval Neural Networks effectively reveal image reconstruction instabilities.
Model based predictions of future trajectories of a dynamical system often suffer from inaccuracies, forcing model based control algorithms to re-plan often, thus being computationally expensive, suboptimal and not reliable. In this work, we propose a model agnostic method for estimating the uncertainty of a model?s pr…
Optimizing noise variance in VAEs balances reconstruction quality and prior regularisation.
problem Balancing reconstruction quality and prior regularisation in VAEs.
method Learning the noise variance in the Gaussian likelihood to balance the ELBO loss.
result Optimizing noise variance improves VAE-generated sample quality and uncertainty.
FisherNet extends Autoencoder using Fisher information for better data reconstruction.
problem Data reconstruction accuracy and model scalability in high-dimensional latent spaces.
method Introduces FisherNet architecture that uses Fisher information to quantify and account for latent space uncertainty.
result FisherNet produces more accurate reconstructions and scales better with latent space dimensions compared to VAE.
New method reconstructs stable sentiment signals from sparse news data.
problem Transforming raw news sentiment outputs into reliable temporal series.
method Modular three-stage pipeline: aggregation, gap filling, and smoothing.
result Three-week lead-lag pattern between reconstructed sentiment and stock prices.
Cake wavelets minimize orientation score uncertainty.
problem Minimizing uncertainty in orientation scores.
method Axiomatically derived wavelets for orientation score lifting.
result Uncertainty gap of cake wavelets is less than 1.1.
PaRCE estimates model confidence for CNNs across various uncertainties.
problem Limited holistic approach to estimating perception model confidence in CNNs.
method Probabilistic and reconstruction-based competency estimation.
result PaRCE best distinguishes between various types of samples and regions.
This paper is the first work to propose a network to predict a structured uncertainty distribution for a synthesized image. Previous approaches have been mostly limited to predicting diagonal covariance matrices. Our novel model learns to predict a full Gaussian covariance matrix for each reconstruction, which permits …
Simplifies VAE for anomaly detection using rate-distortion theory.
problem Anomaly detection in unsupervised learning systems.
method Revisit VAE from information theory, incorporate model uncertainty.
result Competitive performance on benchmark datasets.
Enhances ocean floor mapping with adaptive uncertainty estimates.
problem Inaccurate bathymetric data for precise ocean modeling.
method Block-based conformal prediction with VQ-VAE architecture.
result Significant improvements in reconstruction quality and uncertainty estimation reliability.
Paper speeds up visualization of uncertain data.
problem High computational cost in reconstructing data uncertainties.
method Subdivide data spatially, adaptively reconstructing only necessary values, using GPR kernel and saved data observations to estimate upper bounds for level-crossing probabilities.
result Accurate estimation of value occurrence probabilities with low computation cost.
DEUA detects diffusion-generated images by accounting for different types of uncertainty.
problem Detecting generated images with varying aleatoric and epistemic uncertainty.
method DEUA framework using Laplace approximation for DEU estimation and asymmetric loss function.
result DEUA achieves state-of-the-art performance on large-scale benchmarks.
Bayesian ptychography method reduces overlap for faster imaging.
problem Reduced overlap leads to large data volumes and long acquisition times.
method Generative model combined with MCMC for posterior sampling.
result Framework consistently outperforms iterative reconstruction methods with reduced overlap.
Develops probabilistic models for gene regulatory network inference.
problem Challenges in reconstructing gene regulatory networks from genome-wide data.
method Two complementary frameworks: PMF-GRN and GLM-Prior.
result Probabilistic inference refines regulatory estimates with quantified uncertainty.
Reconstructing the position of an interaction for any dual-phase time projection chamber (TPC) with the best precision is key to directly detecting Dark Matter. Using the likelihood-free framework, a new algorithm to reconstruct the 2-D (x; y) position and the size of the charge signal (e) of an interaction is presente…
A technique called 'prior laundering' uses legacy reconstructions to create uncertainty in Bayesian inverse problems.
problem Uncertainty in Bayesian inverse problems when data is uninformative.
method Using an archive of legacy reconstructions to create uncertainty in the posterior distribution, averaging the legacy posterior over measurements.
result The uncertainty reported in the posterior is inherited from the legacy reconstructions, not from the data itself.
Bayesian framework optimizes 3D view selection for specific tasks.
problem Optimizing 3D view selection for specific tasks in reconstruction.
method Bayesian decision theory, prior and posterior distributions, stochastic surface reconstruction.
result Framework achieves superior performance with fewer views.
This paper tackles high-dimensional uncertainty quantification with semi-supervised learning.
problem High-dimensional uncertainty quantification due to the curse of dimensionality.
method Autoencoder for dimension reduction, DFN for mapping and reconstruction, GP for surrogate modeling, semi-supervised learning for accuracy.
result The framework effectively reduces uncertainty quantification and reliability analysis for high-dimensional problems.
VCL adds uncertainty to contrastive learning models.
problem Lack of uncertainty quantification in contrastive learning methods.
method VCL uses a decoder-free framework that maximizes ELBO with InfoNCE loss and KL divergence.
result VCL provides meaningful uncertainty estimates and matches deterministic baselines in accuracy.
Novel framework uses few data for Bayesian inference in imaging.
problem Uncertainty estimation in machine learning for imaging requires large data volumes.
method Variational inference framework combining few data, domain expertise, and existing datasets.
result Bayesian models achieve state-of-the-art reconstructions with minimal data collection.
Generative models improve MRI reconstruction by learning image structure.
problem Improving MRI image quality from undersampled data.
method Using variational autoencoders (VAEs) to learn image structure and covariance.
result The proposed method outperforms other regularization techniques on MRI datasets.
A new approach to VAEs tackles variance shrinkage using quantile regression.
problem Variance shrinkage in VAEs leads to underestimation of uncertainty.
method Using quantile regression to estimate mean and variance, avoiding shrinkage.
result Our approach effectively detects anomalies and improves lesion detection.
Bayesian model reconstructs time and frequency data robustly.
problem Missing observations and noise in time/frequency data.
method Probabilistic model, Bayesian update, joint reconstruction.
result Effective joint time/frequency reconstruction with missing data.
UA-LQE improves value function learning by selectively erasing uncertain entries in Q-matrix.
problem Improving value function learning in complex reinforcement learning tasks.
method Uncertainty-aware low-rank Q-matrix estimation (UA-LQE) algorithm.
result UA-LQE selectively erases uncertain entries in Q-matrix to improve value function approximation.
In information theory, Fisher information and Shannon information (entropy) are respectively used to quantify the uncertainty associated with the distribution modeling and the uncertainty in specifying the outcome of given variables. These two quantities are complementary and are jointly applied to information behavior…
A new framework reduces inconsistencies in chaotic surrogate modeling.
problem Consistency issues between probabilistic objectives and dynamical system dynamics.
method KAFFEE (Kalman-Aware Framework For Ergodic Emulation), a differentiable extended Kalman filter.
result KAFFEE mitigates the dynamic-probabilistic consistency gap, improving reconstruction and predictive scores.
Stochastic image reconstruction is a key part of modern digital rock physics and materials analysis that aims to create numerous representative samples of material micro-structures for upscaling, numerical computation of effective properties and uncertainty quantification. We present a method of three-dimensional stoch…
Bayesian PINNs tackle uncertainty in inverse problems.
problem Uncertainty quantification in inverse problems.
method Hierarchical Bayesian formulation of PINNs with variational inference and Monte Carlo dropout.
result Quantification of uncertainties in reconstructed images.
Develops a framework for inferring causal relationships in networked data with uncertainty quantification.
problem Extracting reliable inference from complex Hawkes network data with uncertainty.
method Statistical inference framework based on maximum likelihood estimation and concentration inequalities of continuous-time martingales.
result Provides a non-asymptotic confidence set for uncertainty quantification.
3D dust map of the Milky Way improves resolution and accuracy.
problem Reconstructing the 3D dust distribution in the Milky Way.
method Gaussian process regression on spherical coordinates with iterative grid refinement.
result Improved 3D dust map with increased resolution and accuracy.
SVGP KAN integrates uncertainty quantification into Kolmogorov-Arnold networks.
problem Uncertainty quantification in scientific machine learning models.
method Sparse variational Gaussian process inference with Kolmogorov-Arnold topology.
result Demonstrated ability to distinguish aleatoric and epistemic uncertainty in various scientific applications.
In the context of dynamic emission tomography, the conventional processing pipeline consists of independent image reconstruction of single time frames, followed by the application of a suitable kinetic model to time activity curves (TACs) at the voxel or region-of-interest level. The relatively new field of 4D PET dire…
Bayesian method improves parameter reconstruction from many measurements.
problem Efficiently reconstructing parameters from many experimental measurements.
method Bayesian target-vector optimization considering all model outputs.
result Outperforms established optimization methods in accuracy and efficiency.
The paper improves GP regression for sparse sensor data in structural mode shape reconstruction.
problem Reconstructing full-field structural mode shapes from sparse sensor data.
method Physics-Constrained Single-Output Gaussian Process (CONS-SOGP) framework.
result The proposed method provides more accurate and reliable mode shapes.