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.
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Showing 1 to 20 of 23 for “"probabilistic machine learning"”.
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Essays on Probabilistic Machine Learning for Economics
… consists of three essays that explore the use of probabilistic machine learning techniques in combination with information-theoretic concepts to answer economic questions. Over the past years, economists have started applying machine learning methods to a wide range of topics. Probabilistic …
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Probabilistic machine learning algorithms for molecule discovery
… information will be gained from each test. In machine learning, this approach is typically called Bayesian optimisation and has been studied for many other problems, such as tuning hyperparameters of machine learning models. Although in principle Bayesian optimisation can be straightforwardly …
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Photonic probabilistic machine learning using quantum vacuum noise
Probabilistic machine learning is an emerging paradigm which harnesses controllable random sources to encode uncertainty and enable statistical modeling. The pure randomness of quantum vacuum noise, fluctuation of electromagnetic fields even in the absence of a photon, has been utilized for high …
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Probabilistic machine learning for the elimination of thermoacoustic instabilities
… 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 qualitative …
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Applications of Probabilistic Machine Learning Models to Semiconductor Fabrication
… employed for decades, recent developments in machine learning have introduced a wide variety of new methods that can potentially be used to better model, monitor, and control these processes. These methods offer the possibility of being more powerful, scalable, and accurate than traditional …
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Superparamagnetic Tunnel Junctions for Reliable True Randomness and Efficient Probabilistic Machine Learning
… computing algorithms, accelerate machine learning tasks, and enhance hardware security. Recently, superparamagnetic tunnel junctions (sMTJs) have been widely explored for such purposes, leading to the development of limited-scale sMTJ-based systems. Existing sMTJs face significant …
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Probabilistic Machine Learning for Circular Statistics: Models and inference using the Multivariate Generalised von Mises distribution
Probabilistic machine learning and circular statistics—the branch of statistics concerned with data as angles and directions—are two research communities that have grown mostly in isolation from one another. On the one hand, probabilistic machine learning community has developed powerful frameworks …
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Probabilistic Oil and Gas Production Forecasting using Machine Learning
… of the production-forecasting process, using probabilistic 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 …
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UNCERTAINTY QUANTIFICATION OF LANDSLIDE SUSCEPTIBILITY MAPPING USING BAYESIAN NETWORK
… samples (i.e., non-landslide points) for machine learning-based model training, and (3) interpreting the causal relationships among factors influencing landslides and uncertainty propagation in model predictions. To address these knowledge gaps, this work presents results of 1) sensitivity …
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Hierarchical Inference in Gaussian Processes
Hierarchical modelling is a fundamental theme in probabilistic machine learning which relies on the Bayesian interpretation on probability. The starting requirement for these models is that all unknowns are treated as random variables with their own respective probability distributions. The central …
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Advances in Compression using Probabilistic Models
… data. One emerging solution lies in applying probabilistic machine learning to capture the data distribution in an unsupervised manner. Once a probabilistic model for the data is defined, variational inference can be used to infer its parameters from data. Variational inference is closely …
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Approximate Inference in Bayesian Neural Networks and Translation Equivariant Neural Processes
It has been a longstanding goal in machine learning to develop flexible prediction methods that ‘know what they don’t know’ — when faced with an out-of-distribution input, these models should signal their uncertainty rather than be confidently wrong. This thesis is concerned with two such …
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Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models
… that have become a standard tool in modern probabilistic machine learning. However, the applicability of Gaussian processes in the large-data regime and in hierarchical probabilistic models is severely limited by analytic and computational intractabilities. It is, therefore, important to …
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Variational Inference and Probabilistic Models for Parametric Partial Differential Equations
… This thesis is an attempt at adapting methods of probabilistic machine learning to create methodological advances in solving various problems relating to PDEs though variational inference and probabilistic models. The work is composed of three contributions. The first contribution lies in creating …
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Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning
Learning is the ability to generalise beyond training examples; but because many generalisations are consistent with a given set of observations, all machine learning methods rely on inductive biases to select certain generalisations over others. This thesis explores how the model structure and …
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Towards efficient tuning of computer systems: auto-structured Bayesian optimization from system metrics
… offers a promising alternative by incorporating probabilistic performance models, it suffers from two limitations. The first is the difficulty of designing these models, which requires expertise in both the system and probabilistic machine learning. The second is scalability issues when dealing …
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Probabilistic Characterization of Sediments Using a Combined Geotechnical and Geophysical Approach
… limitations by developing and integrating novel probabilistic frameworks utilizing data from Portable Free Fall Penetrometers (PFFP) and Chirp sub-bottom profilers for enhanced shallow-water sediment assessment. First, a probabilistic machine learning model, combining Random Forest and a Bayesian …
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Anomaly Detection Through System and Program Behavior Modeling
… mal-intended instruction sequences on a victim machine without injecting external code. Successful exploitation leads to hijacked applications or the download of malicious software (drive-by download attack), which usually happens without the notice or permission from users. In this …
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Improving Deep Learning with Probabilistic Approaches
… in scaling to real-world problems, deep learning is not 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 …
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Precipitation prediction over High Mountain Asia using Gaussian processes
… change. This thesis discusses the application of probabilistic machine learning to better understand and predict precipitation in this area. Probabilistic methods quantify our knowledge of precipitation and improve decision-making under uncertainty. This work centres around one such method, …
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