Overparametrized neural networks retain significant epistemic uncertainty even with sufficient data.
problem Epistemic uncertainty in overparametrized neural networks persists despite model identifiability.
method Analysis of non-identifiability and characterization of residual uncertainty in one-hidden-layer ReLU networks.
result Substantial parameter uncertainty remains even when the underlying function is fully identified.
New method RECAST improves uncertainty calibration in neural networks.
problem Improving uncertainty estimation in neural networks for better predictions.
method Proposed RECAST method combining cosine annealing, warm restarts, and Stochastic Gradient Langevin Dynamics.
result RECAST offers the best calibrated measure of uncertainty compared to recent methods.
Cooperative model disentangles data uncertainties.
problem Disentangling aleatoric and epistemic uncertainties in real-world data.
method Cooperatively trains a variance estimation network with a Bayesian neural network.
result Improves mean estimation and disentangles uncertainties.
Bayesian inference improves neural network predictions by separating aleatoric and epistemic uncertainties.
problem Improving prediction accuracy of neural networks by quantifying and separating uncertainties.
method Approximated posterior distributions using deep ensembles for various neural network architectures.
result Prediction accuracy depends on both aleatoric and epistemic uncertainties, not just marginalized uncertainty.
Survey of uncertainty in neural networks.
problem Understanding and quantifying uncertainty in neural networks predictions.
method Review of recent advances, challenges, and potential opportunities in uncertainty estimation.
result Comprehensive overview of uncertainty sources and approaches.
Bayesian neural networks struggle with uncertainty estimates between regions.
problem Limited expressiveness of predictive uncertainty estimates in between regions.
method Compared mean-field variational inference (MFVI) with linearised Laplace approximation.
result Linearised Laplace approximation handles 'in-between' uncertainty better.
Bayesian neural network models improve uncertainty quantification in multivariate regression.
problem Uncertainty quantification in multivariate regression models with heteroscedastic noise.
method Proposes Bayesian Last Layer neural network models and EM algorithms for parameter learning.
result Capable of disentangling aleatoric and epistemic uncertainty.
Unified Bayesian framework for quantifying GNN uncertainty.
problem Quantifying uncertainty in GNN predictions due to modeling errors and measurement uncertainty.
method Unified Bayesian framework with aleatoric uncertainty from probabilistic links and feature noise, and epistemic uncertainty from model parameter distribution. Uses Assumed Density Filtering for aleatoric uncertainty and Monte Carlo dropout for model parameter uncertainty.
result Bayesian model performs similarly to frequentist model and provides additional uncertainty information.
Proposes a new criterion for reliable uncertainty estimation in deep neural networks.
problem Inability of existing approaches to provide reliable uncertainty estimates for deep neural networks.
method Develops a density uncertainty layer architecture that satisfies the proposed criterion.
result Density uncertainty layers provide more reliable uncertainty estimates and robust out-of-distribution detection.
Enhances neural network regression performance by modeling weight and variance uncertainty.
problem Improving predictive performance of neural networks for regression tasks.
method Extended Blundell's framework to include variance uncertainty, using a full posterior distribution over variance parameters.
result Explicitly modeling variance uncertainty improves generalization of Bayesian neural networks.
Proposes a new method for uncertainty estimation in neural networks.
problem Estimating uncertainty in neural networks.
method Samples outputs from Gaussian distributions parametrized by mean and variance sub-layers.
result Achieves better uncertainty quality than other methods.
USNRT uses tree-structured learning to improve uncertainty quantification of variance networks.
problem Improving uncertainty quantification of variance networks.
method Tree-structured local neural network model that partitions feature space into regions for training region-specific neural networks to predict mean and variance.
result USNRT shows superior performance in estimating uncertainty with variances on UCI datasets compared to recent methods.
Unified method for input, data, and model uncertainty in neural networks.
problem Uncertainty in neural network inputs and outputs.
method Propagating uncertainty through inputs using a unified formulation.
result More stable decision boundaries with input noise, and propagation of input uncertainty to model outputs.
Enhances uncertainty estimation in neural networks using Dirichlet-based MC Dropout.
problem Deterministic predictions without uncertainty estimates in neural networks.
method Integrates Dirichlet-based framework within Monte Carlo Dropout.
result Improves quality of uncertainty estimates in deep learning models.
Paper improves uncertainty quantification in PINNs using error bounds and solution bundles.
problem Uncertainty quantification in PINNs for differential equation systems.
method Two-step procedure with Bayesian Neural Networks and heteroscedastic variance.
result Improved uncertainty estimation over PINNs solutions in differential equation systems.
EKF improves DNN uncertainty quantification with less computation.
problem Uncertainty quantification in deep neural networks.
method Extended Kalman Filtering applied to DNNs.
result EKF reduces computational overhead for uncertainty quantification.
The paper tackles exploration in reinforcement learning by accounting for two types of uncertainty.
problem Directed exploration for reinforcement learning agents with two sources of uncertainty.
method The approach involves learning parametric and return uncertainty with deep neural networks and estimating them in a Double Uncertain Value Network.
result The policy is derived from learned distributions based on Thompson sampling, showing improvement in domains with strong exploration challenges.
Simplifies neural regression by combining two sub-networks for predictions and uncertainties.
problem Neural networks underestimate uncertainty, leading to overly confident predictions.
method Extends IRLS to a two-sub-network approach with shared representations and complementary loss functions.
result Proposed network is simpler to implement and more robust to uncertainty variations.
CUQ-GNN adapts uncertainty quantification for graph data, improving on GPN.
problem Defining meaningful uncertainty on graph data with domain-specific characteristics.
method Combines Graph Neural Networks with Posterior Networks using Normalizing Flows.
result CUQ-GNN produces more flexible and effective uncertainty estimates.
New method improves estimation of neural network aleatoric uncertainty.
problem Existing methods overestimate aleatoric uncertainty in neural networks.
method Proposes a new de-noising method to estimate data uncertainty more accurately.
result Demonstrates better approximation of actual data uncertainty.
Bayesian neural networks quantify uncertainty in molecular property predictions.
problem Uncertainty in molecular property predictions due to limited data quality and quantity.
method Bayesian neural networks to decompose and quantify model- and data-driven uncertainties.
result Data noise significantly affects data-driven uncertainties in molecular property predictions.
Bayesian Neural Networks improve uncertainty estimation in deep learning.
problem Lack of robustness and sensitivity to out-of-distribution samples in DNNs.
method Empirical evaluation of Bayesian Neural Networks against point estimate DNNs.
result Bayesian Neural Networks provide better uncertainty quantification and performance.
SON learns SPDE solutions and uncertainty from noisy data.
problem Uncertainty quantification in SPDEs with unknown model uncertainties.
method Combining DeepONet and SNNs, SON models stochasticity and predicts uncertainty.
result SON accurately captures solution structure and quantifies predictive uncertainty.
Proposes a method to quantify uncertainty in graph neural networks for node classification.
problem Uncertainty in graph neural networks for node classification.
method Bayesian uncertainty propagation (BUP) method embedding GNNs in a Bayesian framework.
result Demonstrates superior performance of the proposed method on benchmark datasets.
Bayesian neural networks enhance uncertainty estimation in graph contrastive learning.
problem Uncertainty in graph contrastive learning when labeled data is scarce.
method Variational Bayesian neural networks for uncertainty estimation.
result Improved uncertainty estimation and downstream performance on semi-supervised node-classification tasks.
This study analyzes economic policy uncertainty indices using visibility graphs.
problem Understanding the role of economic policy uncertainty in global economies.
method Visibility graph algorithm applied to economic policy uncertainty indices.
result The economic policy uncertainty indices exhibit persistent behavior and scale-free networks.
New method extracts aleatoric and epistemic uncertainties from regression-based neural networks.
problem Need for principled uncertainty reasoning in machine learning systems.
method Learning evidential distributions for aleatoric and epistemic uncertainties.
result Allows for the simultaneous extraction of both uncertainties without sampling or out-of-distribution data.
Probabilistic deep learning uses neural networks and models to handle uncertainty.
problem Handling uncertainty in deep learning models.
method Two approaches: probabilistic neural networks and deep probabilistic models.
result TensorFlow Probability library supports both approaches.
Unified model estimates uncertainty types for deep networks.
problem Accurate uncertainty estimation for deep neural networks.
method Unified hierarchical model combining Bayesian, latent density, and classification methods.
result Unified model efficiently estimates uncertainty for model capacity, data, and open set.
Select-DC reduces GFLOPS for uncertainty estimation in neural networks.
problem Computational inefficiency in estimating model uncertainty for low-latency applications.
method Select-DC uses a subset of layers to model epistemic uncertainty with MCDC, reducing GFLOPS.
result Significant reduction in GFLOPS required for uncertainty estimation with marginal performance loss.
Few Bayesian layers near output capture model uncertainty in deep learning.
problem Capturing model uncertainty in deep learning models.
method Varying the number and position of Bayesian layers in a network, comparing performance on active learning with MNIST dataset.
result Few Bayesian layers near the output can capture model uncertainty efficiently.
Proposes a method to identify critical regions in neural networks using adversarial attacks.
problem Capturing uncertainty in neural networks near decision boundaries.
method Adversarial attack method to derive uncertainty from input perturbations.
result The proposed method outperforms other uncertainty methods in capturing model uncertainty.
The paper introduces a method to quantify uncertainty in neural networks without parametric assumptions.
problem Uncertainty quantification for neural network predictions.
method Nonparametric estimation of conditional label distribution using Nadaraya-Watson kernel.
result The method effectively disentangles aleatoric and epistemic uncertainties.
Single-pass method estimates neural network uncertainty.
problem Uncertainty estimation in deep learning requires multiple passes.
method Probabilistic reasoning over neural network depths.
result Single forward pass for uncertainty estimation.
Graph Posterior Network improves uncertainty estimation for node classification in interdependent graphs.
problem Uncertainty quantification for non-independent node-level predictions in graphs.
method Derives axioms for expected predictive uncertainty, proposes Graph Posterior Network (GPN) which performs Bayesian posterior updates.
result GPN outperforms existing approaches for uncertainty estimation in semi-supervised node classification.
New framework models uncertainty with uncertainty variables.
problem Modeling uncertainty in state estimation and inference.
method Developed uncertainty variables, sets, and graphical models.
result Preserves independence properties and builds useful concepts.
New framework quantifies uncertainties in neural network explanations.
problem Lack of methods to quantify uncertainties in neural network explanations.
method Converts any explanation method into a Bayesian neural network method, modeling uncertainties.
result Allows quantification of explanation uncertainties and appropriate confidence levels.
Proposes a simple method to explain aleatoric uncertainty in neural networks.
problem Lack of transparent explanations for uncertainty estimates in AI models.
method Adapting a neural network with Gaussian output to estimate predictive variance and applying explainers to the variance output.
result The proposed method explains uncertainty more reliably than complex approaches and outperforms them in most settings.
Qualitative analysis of MC dropout for NN model uncertainty.
problem Measuring uncertainty in neural network models.
method Mathematical formulation of Monte Carlo dropout and its benefits/costs in NN models.
result Potential benefits and associated costs of using MC dropout in NN models.
Delta Variances efficiently estimate epistemic uncertainty in neural networks.
problem Mitigating uncertainty in neural networks with limited data.
method Delta Variances, a computationally efficient algorithm for epistemic uncertainty quantification.
result Empirically competitive results with a single gradient computation.
Bayesian uncertainty estimation for batch normalized networks.
problem Estimating uncertainty in deep networks trained with batch normalization.
method Equivalence of batch normalization to Bayesian inference; conventional architectures with uncertainty estimates.
result Significant improvement in uncertainty estimation quality compared to baselines.
Improved neural network regression uncertainty estimation.
problem Neural networks lack classical uncertainty due to finite data.
method Bootstrapped Deep Ensembles, incorporating parametric bootstrap.
result Significantly improved uncertainty estimation compared to standard Deep Ensembles.
The paper evaluates and improves uncertainty estimates in neural networks for safety-critical applications.
problem Quantifying uncertainty in neural networks for safety-critical systems.
method Proposes a statistical test for evaluating uncertainty realism in neural networks and transfers a classification architecture to image-to-image tasks.
result The variational U-Net architecture significantly improves uncertainty realism in image-to-image tasks compared to a plain model.
Proposes a method to propagate uncertainty in neural networks for sparse coding.
problem Uncertainty in neural networks for sparse coding.
method Representing the target vector as a spike and slab distribution at each layer, deriving gradients of normalisation constants, and using Bayesian inference.
result Designs a novel Bayesian neural network for sparse coding.
New method reduces uncertainty in deep neural networks with minimal computation.
problem Uncertainty in over-parameterized neural networks hinders reliability and statistical guarantees.
method Procedural-noise-correcting (PNC) predictor and resampling methods.
result Asymptotically exact-coverage confidence intervals constructed with minimal computation.
RegVar quantifies uncertainty in deep learning networks by measuring sensitivity to regularization.
problem Uncertainty quantification in deep learning networks, especially for large networks.
method RegVar method based on variation due to regularization, implemented during fine-tuning phase.
result RegVar provides rigorous uncertainty estimates that recover Bayesian deep learning approximations.
Bayesian Neural Networks show unexpected collapse of epistemic uncertainty with large models and little data.
problem Unexpected collapse of epistemic uncertainty in Bayesian Neural Networks.
method Experiments with varying model size and training data size.
result Epistemic uncertainty collapses in the presence of large models and sometimes little data.
This paper connects noise injection to Bayesian inference for neural networks, improving model uncertainty.
problem Improving the reliability and confidence of neural network predictions through uncertainty quantification.
method Introducing noise into neural network parameters during training and inference to estimate prediction uncertainty.
result The MCNI method outperforms baseline models in regression and classification tasks.