Real-time uncertainty estimation for computer vision tasks.
problem Real-time inference of uncertainty in deep learning models.
method Uncertainty-Aware Distribution Distillation method for fast inference.
result Significantly reduced inference time with improved uncertainty and predictive performance.
CRC method provides tighter uncertainty intervals for CT images.
problem Expressing uncertainty in CT images in clinically meaningful terms.
method Semantically adaptive CRC procedure leveraging length minimization.
result Valid coverage of ground-truth images with tighter uncertainty intervals.
Framework disentangles deep feature uncertainty for efficient inference.
problem Inference-time uncertainty estimation for reliable decision-making.
method Uncertainty-Guided Inference-Time Selection framework.
result Significantly tighter prediction intervals and 60% compute reduction.
New methods accelerate NCGP inference by trading computation for uncertainty.
problem Prohibitively expensive exact inference in NCGPs for large datasets.
method Iterative methods explicitly modeling approximation error, leveraging parallel computing.
result Significant acceleration of posterior inference compared to baselines.
New GP methods account for both data and computational uncertainty.
problem Approximation error in Gaussian process models.
method Develops a new class of methods to estimate combined uncertainty.
result Proves convergence and decomposability of combined posterior covariance.
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.
New method calibrates neural network uncertainty for medical images.
problem Uncalibrated probabilistic outputs from deep neural networks in medical diagnosis.
method Functional space variational inference for Bayesian neural networks.
result Better calibrated uncertainty estimates at lower computational cost.
New BNN architectures reduce computational cost for uncertainty quantification.
problem High computational cost in Bayesian neural networks.
method Partial trace-class Bayesian neural networks (PaTraC BNNs).
result Comparable uncertainty quantification with fewer parameters.
Study decomposes uncertainty in HK-distribution parameter estimation for QUS.
problem Uncertainty in HK-distribution parameter estimation for quantitative ultrasound.
method Bayesian Neural Networks (BNNs) for parameter estimation and uncertainty decomposition.
result Decomposes total predictive uncertainty into epistemic and aleatoric components.
A new method quantifies uncertainty in brain injury simulations.
problem High computational cost and high-dimensional inputs/outputs limit traditional UQ methods for biofidelic head models.
method Two-stage, data-driven manifold learning framework using Gaussian kernel-density estimation, diffusion maps, and Grassmannian diffusion maps.
result Surrogate models reduce computational cost while providing highly accurate approximations of the computational model.
We present a sampling-free approach for computing the epistemic uncertainty of a neural network. Epistemic uncertainty is an important quantity for the deployment of deep neural networks in safety-critical applications, since it represents how much one can trust predictions on new data. Recently promising works were pr…
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.
Bayesian optimization reduces hyperparameter tuning cost for stochastic models.
problem Hyperparameter tuning under uncertainty in noisy function evaluations.
method Bayesian optimization framework for scale parameter in stochastic models, using statistical surrogate and closed-form optimizer.
result Significant reduction in computational cost (40 times fewer data points, 40-fold reduction in cost).
TULiP estimates uncertainty for deep learning models safely.
problem Reliable uncertainty estimation for deep learning models in the open world.
method TULiP considers a hypothetical perturbation, bounds its effect, and computes uncertainty from sampled predictions.
result TULiP achieves state-of-the-art performance in OOD detection benchmarks.
While deep neural networks have become the go-to approach in computer vision, the vast majority of these models fail to properly capture the uncertainty inherent in their predictions. Estimating this predictive uncertainty can be crucial, for example in automotive applications. In Bayesian deep learning, predictive unc…
Study wSAA for contextual decisions, improving uncertainty quantification under computational constraints.
problem Uncertainty quantification limitations in wSAA for contextual stochastic optimization.
method Establish central limit theorems and asymptotic-normality-based confidence intervals for optimal costs.
result Over-optimizing can mitigate misspecification and preserve asymptotic normality, albeit at a slower convergence rate.
Framework for pricing waterfall structures using simulation and uncertainty modeling.
problem Pricing complex structured finance instruments under uncertainty.
method Simulation-based uncertainty modeling, calibrated probability distributions, PyTorch implementation, Adjoint Algorithmic Differentiation (AAD).
result Efficient gradient computation for risk sensitivity analysis and optimization.
Deep neural network controllers for autonomous driving have recently benefited from significant performance improvements, and have begun deployment in the real world. Prior to their widespread adoption, safety guarantees are needed on the controller behaviour that properly take account of the uncertainty within the mod…
ABNN converts pre-trained DNNs into BNNs for reliable uncertainty quantification.
problem Uncertainty quantification in deep neural networks (DNNs) is challenging and critical for real-world applications.
method Adaptable Bayesian Neural Network (ABNN) that transforms pre-trained DNNs into BNNs with minimal overhead.
result ABNN achieves state-of-the-art performance in image classification and semantic segmentation tasks.
New method improves calibration of BayesCG for better uncertainty quantification.
problem Bayesian conjugate gradient method's poor calibration limits its utility.
method Randomized postiteration strategy to enhance posterior calibration.
result The method improves the distribution of posterior errors and enhances uncertainty quantification.
ConformalHDC improves HDC's uncertainty quantification for neuromorphic learning.
problem Lack of rigorous uncertainty quantification in Hyperdimensional Computing.
method Combines conformal prediction with HDC's efficiency, proposing set-valued and point-valued formulations.
result Demonstrates improved robustness and accuracy in decoding neural stimulus information.
Extended Kalman Filtering (EKF) can be used to propagate and quantify input uncertainty through a Deep Neural Network (DNN) assuming mild hypotheses on the input distribution. This methodology yields results comparable to existing methods of uncertainty propagation for DNNs while lowering the computational overhead con…
Proposes hinge-Wasserstein to improve uncertainty estimation in regression tasks.
problem Estimating multimodal aleatoric uncertainty in regression tasks from images.
method Regression-by-classification paradigm with hinge-Wasserstein loss.
result Hinge-Wasserstein loss improves uncertainty estimation on challenging tasks.
Paper introduces a Gaussian Process for operator learning in computational mechanics.
problem Efficient and accurate solutions for large datasets with reliable uncertainty quantification.
method Gaussian Process (GP) embedded in a neural operator framework with stochastic dual descent (SDD) algorithm.
result Improves GP resolution independence and scalability for high-dimensional and non-linear systems.
New method calibrates uncertainty in molecular property predictions.
problem Uncalibrated uncertainty estimates in molecular property predictions.
method Message Passing Neural Networks with calibrated probabilistic predictive distribution.
result Accurate molecular formation energy predictions with well-calibrated uncertainty.
New method explains predictive uncertainty by focusing on second-order effects.
problem Explaining predictive uncertainty in machine learning models.
method CovLRP, CovGI, etc., based on second-order effects.
result Predictive uncertainty is dominated by second-order effects.
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.
Many neural networks use the tanh activation function, however when given a probability distribution as input, the problem of computing the output distribution in neural networks with tanh activation has not yet been addressed. One important example is the initialization of the echo state network in reservoir computing…
Neural networks simplify uncertainty quantification of locally nonlinear systems.
problem Estimating statistics of responses in large-scale locally nonlinear dynamical systems.
method Decomposes response into nominal linear system and a neural network-estimated pseudoforce.
result Neural networks can efficiently estimate pseudoforce containing nonlinear and uncertain information.
A method to select important experts for Gaussian processes to balance computational efficiency and uncertainty quantification.
problem Balancing computational efficiency and uncertainty quantification in Gaussian processes for big data.
method Using graphical models to select important experts and aggregate their predictions while ensuring uncertainty quantification.
result Substantially reduces computational cost of aggregating dependent experts while ensuring calibrated uncertainty quantification.
Polynomial chaos surrogates handle intrinsic noise in stochastic models.
problem Handling intrinsic noise in stochastic models with parametric uncertainty.
method Developed a PCE surrogate on a joint space of intrinsic and parametric uncertainty using Rosenblatt transformations and Karhunen-Loeve expansion.
result Quantified intrinsic noise contribution to model output variance using PCE Sobol indices.
Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computa…
Efficiently estimates uncertainty for LLM-based entity linking in tabular data.
problem Accurate and reliable uncertainty estimates for LLM-based entity linking in tabular data.
method Self-supervised approach using token-level features for single-shot inference.
result Effective uncertainty estimates detected at a fraction of computational cost.
Neural Bootstrapper reduces bootstrapping cost for deep neural networks.
problem Computational burden in bootstrapping deep neural networks.
method Neural Bootstrapper learns to generate bootstrapped neural networks through single model training.
result Neural Bootstrapper outperforms bagging methods with lower computational cost.
Enhances activity recognition in wearable computing with context awareness and uncertainty quantification.
problem Context-dependent activity recognition and unknown contexts in wearable computing.
method Developed the α-{eta} network coupled with uncertainty quantification (UQ) based on maximum entropy.
result Improved accuracy and F-score by 10% through high-level context identification.
New CAGP-GS framework improves Gaussian process uncertainty quantification.
problem Scaling issue in Gaussian processes for large datasets.
method Calibrated probabilistic linear solvers for reduced complexity.
result CAGP-GS framework provides more realistic uncertainty quantification.
UVU simplifies value uncertainty quantification in RL.
problem Estimating epistemic uncertainty in value functions for reinforcement learning.
method UVU uses squared prediction errors between an online learner and a fixed, randomly initialized target network, incorporating policy-conditional value uncertainty.
result UVU achieves equal performance to large ensembles on challenging offline RL settings, with computational savings.
A new method for uncertainty estimation in neural networks using existing optimization steps.
problem Uncertainty quantification in deep neural networks.
method L2M: Practical posterior Laplace approximation with optimization-driven second moment estimation.
result L2M method yields reasonable results without requiring changes in models or extra computational steps.
Study trade-offs between statistical and computational efficiency in variational inference.
problem Optimizing statistical accuracy vs. computational efficiency in Bayesian inference.
method Case study on Gaussian inferential models with diagonal plus low-rank precision matrices, analyzing Bayesian posterior inference and frequentist uncertainty quantification errors.
result Lower-rank models reduce variance and accelerate convergence but increase posterior inference error.
New method for uncertainty analysis in TabPFN, a state-of-the-art tabular transformer.
problem No method for uncertainty decomposition in TabPFN.
method Casted as a Bayesian predictive inference problem, derived variance estimators using predictive CLT.
result Fast to compute credible bands that target epistemic uncertainty and achieve near-nominal frequentist coverage.
We provide single-model estimates of aleatoric and epistemic uncertainty for deep neural networks. To estimate aleatoric uncertainty, we propose Simultaneous Quantile Regression (SQR), a loss function to learn all the conditional quantiles of a given target variable. These quantiles can be used to compute well-calibrat…
Optimizes ellipsoids for uncertainty regions in parameter estimation.
problem Learning minimal volume uncertainty ellipsoids for parameter estimation.
method Differentiable optimization approach using neural networks to approximate optimal ellipsoids.
result Approximately computed ellipsoids are smaller and more accurate than existing methods.
A new RL method handles uncertainty and constraints in real-time optimization.
problem Real-time optimization under process uncertainty and constraints.
method Chance-constrained reinforcement learning to handle probabilistic state constraints.
result Satisfies process constraints with high probability in real-time.
SMURF-THP improves Transformer Hawkes process models by providing uncertainty quantification.
problem Uncertainty quantification for Transformer Hawkes process predictions.
method Score matching for learning the score function of event arrival times.
result SMURF-THP outperforms likelihood-based methods in confidence calibration.
Quantized BNNs maintain uncertainty estimation quality despite reduced precision.
problem Reduced precision in BNNs due to quantization.
method Quantized BNNs with 32-bit weights and activations compressed to 16-bit integers.
result Uniform quantization does not significantly degrade uncertainty estimation quality.
Proposes a Bayesian approach to explain, justify, and quantify uncertainty in DNNs.
problem Lack of transparency and confidence in DNNs for critical applications.
method Bayesian approach to extract explanations, justifications, and uncertainty estimates from black box DNNs.
result Improves interpretability and reliability of DNNs, validated on CIFAR-10.
Machine learning combines high- and low-fidelity models for efficient uncertainty quantification and optimization.
problem Efficiently combining high- and low-fidelity models for uncertainty quantification and optimization.
method Machine learning-based multi-fidelity methods for uncertainty quantification and optimization.
result Unified perspective on multi-fidelity priors for optimization.
Enhances uncertainty modeling in random PDEs using PINNs and generative models.
problem Uncertainty in complex systems modeled by random PDEs.
method Combines Physics-Informed Neural Networks (PINNs) with generative modeling techniques.
result Systematic control of uncertainty with maintained predictive accuracy.