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Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.

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Showing 1 to 13 of 13 for “"Bayesian Neural Network"”.

  1. A Bayesian neural network for censored survival data

    Wong, HLiverpool John Moores University2001

    liverpool-jm Repository record

  2. Data assimilation into physics-based thermoacoustic models using Bayesian neural network ensembles

    Croci, Maximilian LouisUniversity of Cambridge2022

    … method is proposed, which uses a heteroscedastic Bayesian neural network ensemble (BayNNE) trained on a library of simulated flame fronts with known parameters to infer the parameters and uncertainties of the physics-based model. Generating the library of simulated flame fronts and training the …

    cambridge Repository record

  3. Accurate Uncertainty Quantification and Explainable Artificial Intelligence in Machine Learning Models for Toxicological Risk Assessment

    Gong, ChenUniversity of Cambridge2023

    … three different modelling methodologies (Bayesian bootstrapping, conformal prediction, and Bayesian neural networks) on a diverse dataset of 21 toxicologically relevant targets identified by Allen et al. (2022). Metrics to evaluate uncertainty quantification are defined and four …

    cambridge Repository record

  4. Predicting Flavonoid UGT Regioselectivity with Graphical Residue Models and Machine Learning.

    Jackson, Arthur Rhydon2009

    … nearest neighbor, support vector machine, and Bayesian neural network classifiers. Improvements over nearest neighbor classifications relying on standard alignment similarity scores are reported.</p>

    etsu Repository record

  5. Measuring Machine Learning Model Uncertainty with Applications to Aerial Segmentation

    Cotton, Kevin James2022

    … for measuring epistemic uncertainty and the Bayesian neural network. The latter replaces each trained weight with a random variable with which we approximate the true, unknown distribution of each weight with a two parameter (mean and variance) normal distribution. Bayes by Backprop trains …

    claremont Repository record

  6. Probabilistic Oil and Gas Production Forecasting using Machine Learning

    Andrais, RobertMassachusetts Institute of Technology2021

    … machine learning (ML) techniques. A Bayesian Neural Network successfully modelled a complex shale gas reservoir system (Eagle Ford), generating a production forecast with 5% mean absolute percent error. This result is 10%–35% more accurate than traditional decline curve analysis. …

    mit Repository record

  7. Advanced Bayesian Monte Carlo Methods for Inference and Control

    Sell, TorbenUniversity of Cambridge2021

    … ubiquitous tool in modern statistics. Under the Bayesian paradigm, they are used for estimating otherwise intractable integrals arising when integrating a function $h$ with respect to a posterior distribution $\pi$. This thesis discusses several aspects of such Monte Carlo methods. The first …

    cambridge Repository record

  8. Probabilistic machine learning for the elimination of thermoacoustic instabilities

    Sengupta, UshnishUniversity of Cambridge2023

    … The current thesis demonstrates how Bayesian machine learning techniques may be of benefit when modeling, designing against and trying to avoid thermoacoustic instabilities. We show that Bayesian Neural Network can be used to assimilate model parameters from flame data and make our …

    cambridge Repository record

  9. Leveraging Multimodal Perspectives to Learn Common Sense for Vision and Language Tasks

    Lin, XiaoVirginia Tech2017

    … scoring function for deep VQA models under the Bayesian Neural Network framework. Once trained with a large initial training set, a deep VQA model is able to efficiently query informative question-image pairs for answers to improve itself through active learning, saving human effort on …

    vt Repository record

  10. Machine learning methods modeling waveform, multi-parameter full waveform inversion, and uncertainty quantification

    Zhang, TianzeGraduate Studies2024

    … minima. In this thesis, I propose recurrent neural network (RNN) isotropic elastic FWI. Then, I proposed the elastic implicit full waveform inversion. Instead of directly updating the elastic parameters like in the conventional FWI, I use neural networks to generate elastic models and update …

    calgary Repository record

  11. Niobium in Microalloyed Rail Steels

    Ray, AUniversity of Cambridge

    … but wear resistance improves significantly. A Bayesian neural network model has been developed to estimate the wear of rails. Predicted trends have been found consistent with metallurgical experience and the perceived noise levels are consistent with reasonable repeatability of the wear testing …

    cambridge Repository record

  12. Surface Ozone and Population Health

    Sun, ZheUniversity of Cambridge2022

    … a more conservative 2-stage enhanced space-time neural network ensembler is optimised to fuse 57 simulations, both of which have revealed outstanding performances (Chapter 4). The conventional approach is computationally cheaper and achieves slightly higher accuracy, but at the expense of …

    cambridge Repository record

  13. Uncertainty Estimation on Natural Language Processing

    He, JianfengVirginia Tech2024

    … seamlessly integrate with different Deep Neural Networks. Extensive experiments with ablation settings are conducted on four real-world datasets, resulting in consistently competitive improvements. Our second topic focuses on uncertainty estimation on few-shot text classification (UEFTC), …

    vt Repository record