Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 62 for “"Uncertainty Estimation"”.
-
Uncertainty Estimation on Natural Language Processing
… an NLP model's predictions, which is our focus-uncertainty estimation on NLP. Though many works have researched uncertainty estimation or NLP, the combination of these two domains is rare. This is because most NLP research emphasizes model prediction performance but tends to overlook the …
-
Uncertainty Estimation for Single Stage Object Detection
… applications, necessitating robust methods for uncertainty quantification. Although full Bayesian inference would provide the most principled treatment of uncertainty, it is computationally impractical or even infeasiblefor modern largescale models and real-time detection pipelines. However, the …
-
Bayesian networks for uncertainty estimation in the response of dynamic structures
The dissertation focuses on estimating the uncertainty associated with stress/strain prediction procedures from dynamic test data used in turbine blade analysis. An accurate prediction of the maximum response levels for physical components during in-field operating conditions is essential for …
-
Uncertainty Estimation in Deep Learning with application to Spoken Language Assessment
… robust, interpretable and tractable estimates of uncertainty in a model's predictions in order to construct safer Machine Learning systems. This is crucial to applications where the cost of an error is high, such as in autonomous vehicle control, high-stakes automatic proficiency assessment and in …
-
Extrapolation-based Discretization Error and Uncertainty Estimation in Computational Fluid Dynamics
… in unpredictable convergence rates introducing uncertainty in discretization error estimates. To account for the additional uncertainty, various discretization uncertainty estimators have been developed. The goal of this work is to evaluation discretization error and discretization uncertainty …
-
Regularization, Uncertainty Estimation and Out of Distribution Detection in Convolutional Neural Networks
Classification is an important task in the field of machine learning and when classifiers are trained on images, a variety of problems can surface during inference. 1) Recent trends of using convolutional neural networks (CNNs) for various machine learning tasks has borne many successes and CNNs …
-
Novel Machine Learning Models Based Uncertainty Estimation and Sequential Predictions for Blockchain Networks
… learning models are developed in a novel way and uncertainty quantification methods are exploited to detect illicit activities in the public blockchain. Various supervised learning models are thoroughly explored using two datasets derived from Bitcoin and Ethereum blockchains. The unexpected …
-
Physics-Informed, Data-Driven Framework for Model-Form Uncertainty Estimation and Reduction in RANS Simulations
… in mission-critical applications, the estimation and reduction of model-form uncertainties in RANS models have attracted attention in the turbulence modeling community. In this work, I focus on estimating uncertainties stemming from the RANS turbulence closure and calibrating …
-
Nature's Sensors: Using Plants as an Alternative Monitoring Approach for Subsurface Contamination
… Argonne phytoremediation site. The impacts on uncertainty estimation and optimal sampling schemes at the site from the inclusion of plant sampling were evaluated. Four methods were compared while evaluating uncertainty; entropy was found to be the most sensitive measure of uncertainty …
-
Conformal Methods for Efficient and Reliable Deep Learning
… a model-agnostic and distribution-free uncertainty estimation framework. We develop both theory and practice for leveraging uncertainty estimation to build adaptive models that are cheaper to run, have desirable performance guarantees, and are general enough to work well in many …
-
Quantifying Uncertainty in Model Evaluation
… ways to account for model assessment and uncertainty estimation, the academic community hasn’t reached final consensus for a widely used standard. Through bridging the gap between post-hoc and resampling-based frameworks, this proposal seeks to offer validated approach to estimating …
-
Improving Deep Learning with Probabilistic Approaches
… without flaws. In particular, it struggles with uncertainty quantification and data efficiency. Probabilistic methods, while currently somewhat underappreciated by the wider machine learning community, provide calibrated uncertainty estimates and tend to shine in the low-data regime. It would …
-
Particle Image Velocimetry Correlation Signal-to-noise Metrics, Particle Image Pattern Mutual Information and Measurement uncertainty Quantification
… correlation and ultimately the accuracy and uncertainty of the produced PIV measurements. Hence we posit that the correlation signal-to-noise-ratio (SNR) metrics calculated from the correlation plane can be used to quantify the quality of the correlation and the resulting uncertainty of an …
-
Estimating Uncertainty in HSPF based Water Quality Model: Application of Monte-Carlo Based Techniques
To propose a methodology for the uncertainty estimation in water quality modeling as related to TMDL development, four Monte Carlo (MC) based techniques—single-phase MC, two-phase MC, Generalized Likelihood Uncertainty Estimation (GLUE), and Markov Chain Monte Carlo (MCMC) —were applied to a …
-
Uncertainty in Recommender Systems
… tools that we focus on in this dissertation are uncertainty estimators. The problem of uncertainty is relevant to Recommender Systems in at least two ways: prediction uncertainty and label uncertainty. Prediction uncertainty is the expected imprecision of the predictions given by the system's …
-
The clinical, computational, and neuropharmacological determinants of learning under uncertainty
Uncertainty is ubiquitous in our interaction with the world, and hence adaptive behaviour relies on the appropriate estimation of such uncertainty. Associative learning tasks provide a framework within which researchers can induce different types of uncertainty in order to see how the participant …
-
Development of a modelling framework for integrated catchment flood risk management
… on the storm runoff, leading to considerable uncertainty in their effectiveness in terms of mitigating flood risk. A new modelling framework for design, assessment, and uncertainty estimation of such distributed, nature-based schemes is developed. An implementation of a semidistributed runoff …
-
Multi-Level Quantization of Stochastic Variational Inference based Bayesian Neural Networks
… power of standard neural networks with the uncertainty estimation capabilities of Bayesian Inference, offering a robust framework to address challenges such as overconfidence and overfitting. However, the inherent complexity of BNNs —due to the use of weight distributions— renders the …
-
Probabilistic Models for Droughts: Applications in Trigger Identification, Predictor Selection and Index Development
… However, this method does not yield model uncertainty, and is very limited for forecasting upcoming droughts. The primary goal of this thesis is to develop and implement methods that incorporate uncertainty estimation into drought characterization, thereby enabling more informed and better …
Page 1 of 4