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 33 for “"Aleatoric"”.
-
Measuring Machine Learning Model Uncertainty with Applications to Aerial Segmentation
… Uncertainty is divided into two types: aleatoric and epistemic. Aleatoric uncertainty arises from variations in training data and can be the result of poor training data or an inherently stochastic observation. Epistemic uncertainty arises from predicting on inputs that are out of class …
-
Learning-Based Complex Terrain Navigation Under Uncertainty
… the proposed method efficiently quantifies both aleatoric and epistemic uncertainty by learning discrete traversability distributions and probability densities of the traversability predictor’s latent features. Leveraging evidential deep learning, this work parameterizes Dirichlet distributions …
-
Deep Learning For Surrogate Modeling And Uncertainty Quantification In Science & Engineering
… models that distinguish between epistemic and aleatoric uncertainty. To address epistemic uncertainty in operator learning, it introduces Neural Epistemic Operator Networks (NEON), which integrate the Epistemic Neural Network framework with neural operators for learning function-to-function …
-
Model parameter identification and model class selection in piezoelectric energy harvester based on bayesian inference
… some PEH properties as either deterministic or aleatoric (uncertain) variables. The overall framework offers an elegant approach to calibrate PEH numerical/analytical model or identify variability trends for the PEH manufacturing process.
-
Uncertainty Estimation for Single Stage Object Detection
… architecture, assessing their ability to capture aleatoric and epistemic uncertainty across a corruption-augmented COCO dataset. Various Monte Carlo Dropout configurations with different dropout locations were explored; however, Deep Ensembles offer superior robustness and epistemic uncertainty …
-
Experimental Approaches to the Composition of Interactive Video Game Music
… engines, creating music using the techniques of aleatoric composition, real-time remixing of existing work, and generative synthesisers. The project created music for three ‘open-form’ games : an example of the racing genre (Kart Racing Pro); an arena-based first-person shooter (Counter-Strike : …
-
Meditation for Electronic Valve Instrument and Chamber Jazz Ensemble
… composition that uses a mix of prescriptive and aleatoric elements. The solo part is mostly improvised and uses some of the advanced features of the EVI. It is possible however, that the piece could be performed by some other wind instrument even if that instrument did not have the unusual range …
-
A conductor's study of the choral works of Daniel Asia
… Asia's style of composition evolved from an aleatoric and dissonant style to more lyrical and "accessible." He received commissions from numerous musical organizations. Although Professor Asia's primary compositional focus has been on symphonic music, he has also written five works for …
-
Generative modelling under epistemic uncertainty
… and an inability to distinguish between aleatoric uncertainty (data ambiguity) and epistemic uncertainty (lack of knowledge). This thesis addresses these limitations by establishing a rigorous framework for Random-Set Deep Learning, shifting from point-estimate probabilities to belief …
-
On the Certification of Deep Learning-based Dynamical System Identification
… bias gap in the classical neural network-based aleatoric uncertainty estimators. We identify overestimation issues in existing variance attenuation methods and propose a novel denoising-based approach that provides more accurate estimates of data uncertainty. This method not only applies to …
-
Multi-Level Quantization of Stochastic Variational Inference based Bayesian Neural Networks
… performance and uncertainty prediction of aleatoric and epistemic uncertainty. We first analyze the crucial role of input representation in the quantization process and introduce partition quantization based on thermometer coding to mitigate the impact of input quantization errors. Building …
-
The Architectural Coincidence: guessing consciously, gauging unconsciously
… associated with unconsciousness, representing an aleatoric chancing to fulfill one’s inner possibilities. However, “gauging consciously” and “guessing unconsciously” inevitably happens on a spectrum with two extremes, either limiting or diluting the discipline of architecture. This thesis …
-
Computational Reconstruction and Quantification of Aerospace Materials
… done by first removing the material uncertainty (aleatoric uncertainty), which is the noise that is inherent in the original image representing the experimental data. The epistemic uncertainty that arises from the MRF algorithm is analyzed through the study of the percentage of isolated pixels and …
-
Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes
… optimisation scheme capable of modelling aleatoric uncertainty, and hence theoretically capable of identifying molecules and materials that are robust to industrial scale fabrication processes.
-
Investigations into Message Passing Neural Networks and Polymer Fouling
… accuracy of predicted properties and estimated aleatoric uncertainties while maintaining scalable predictivity. Process fouling is a pervasive problem in ethylene plants. Foulant, or the undesired accumulation of material, has been identified throughout process units of ethylene plants. Fouling …
-
A Portfolio of Original Compositions (Anselm McDonnell)
… in this commentary are: Harmonic Language, Aleatoric Techniques, and Writing for the Voice. These represent the primary areas of my musical interests and prominent features that unite the portfolio works. Each of these aspects will be contextualised by the research of composers who have …
-
Bayesian autoencoders for anomaly detection: Design, uncertainty quantification, and explainability with industrial applications
… anomaly detection, capturing both epistemic and aleatoric components. Communicating uncertainty is necessary for knowing when the predictions are doubtful; filtering away uncertain predictions leaves us with more accurate predictions. To improve the explainability of BAEs, two feature attribution …
-
Advances in Reinforcement Learning for Decision Support
… exploration to separate epistemic knowledge from aleatoric variation in hindsight, propose an algorithmic framework that yields a simple and scalable generalization of curiosity that is robust to all types of stochasticity, and demonstrate state-of-the-art results in a popular benchmark. In the …
-
Confident Learning for Machines and Humans
… writing song lyrics by exploiting the inherent aleatoric uncertainty of language and semantics, and (3) assisted-human-learning in open online courses by depolarizing/diversifying comment rankings to mitigate the majority bias inherent in rankings based on upvotes. In each case, the artificially …
Page 1 of 2