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 165 for “"Data Augmentation"”.
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Data Augmentation and Conformal Prediction
… In this thesis, we explore the impact of data augmentation, a popular computer vision technique, on the performance of conformal predictors. In particular, we present multiple ways of combining data augmentation with conformal prediction by introducing five methods of …
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Data Augmentation with Seq2Seq Models
… results in a significantly expanded training dataset and vocabulary size, but has slightly worse performance when tested on the validation split. Although not as fruitful as we had hoped, our work highlights additional avenues for investigation into selecting more optimal model parameters and …
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ARDA : automatic relational data augmentation for machine learning
This thesis is motivated by two major trends in data science: easy access to tremendous amounts of unstructured data and the effectiveness of Machine Learning (ML) in data driven applications. As a result, there is a growing need to integrate ML models and data curation into a homogeneous system …
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Enhanced Finger Movement Detection Using sEMG by Data Augmentation
… in classifying individual finger movements is data sparsity resulting from device availability, acquisition time, and patient privacy laws. To alleviate the problem of limited training data, we propose to synthetically augment the training data. Although some sEMG data augmentation methods have …
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Data augmentation and data efficiency for low-resource language processing
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01
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Evaluating Data Augmentation with Attention Masks for Context Aware Transformations
… learning from large, pre-trained models and data augmentation are arguably the two most widespread solutions to the problem of data scarcity. However, both methods suffer from limitations that prevent more optimal solutions to natural language processing tasks. We consider that transfer …
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Using Data Augmentation and Stochastic Differential Equations in Spatio Temporal Modeling
… manage the large amount of missing information. Data augmentation techniques are frequently used to infer about missing values, unobserved or latent processes, approximation of continuous time processes that are discretely observed.</p><p>The literature treating the inference when modeling using …
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Data Augmentation and Machine Learning for Risk Assessment in Healthcare Associated Infections
Acquisition and analysis of extensive datasets is, today, a central tool in most research fields. Machine learning provides powerful methods to obtain descriptive and predictive models for the data in many applications. The acquisition of quality information is fundamental for the reliability and …
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Towards answering unanswerable questions: data augmentation for enhanced medical domain question answering
Hospitals store patient information in relational databases known as Electronic Health Records (EHRs). Exist ing EHRs have filter and search options on the front end that are converted to SQL queries at the back end. However, these search and filter options become cumbersome when querying the EHR. …
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Early detection of oesophageal cancer based on colour appearance and colour-based data augmentation
… second part of study focuses on the colour-based data augmentation for an artificial intelligence (AI) enhanced computer diagnosis system to enlarge data set. With regard to colour differences, all four categories overlap to a large extent. Hence it is unlikely that determination of early stage of …
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A class of sequential exploratory general cognitive diagnosis models using a polya-gamma data augmentation strategy
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms
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Studies of Stented Arteries and Left Ventricular Diastolic Dysfunction Using Experimental and Clinical Analysis with Data Augmentation
… their heart was obtained. To augment this data, pressure fields were calculated from the velocity data using an omni-directional pressure integration scheme coupled with a proper-orthogonal decomposition-based smoothing. This technique was selected from a variety of methods from the …
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Unlocking the power of CNN models: enhancing training procedure with evolutionary based search for class-specific data augmentation
Data augmentation (DA) is a critical technique for improving the generalization capa-bilities of Convolutional Neural Networks (CNNs) in image classification tasks. This re-search introduces an automated data augmentation framework that leverages a genetic algorithm (GA) to optimize class-specific …
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Towards Creating Synthetic Data Testbeds for Research
Insurance datasets are generally private in order to protect user information, making it difficult for the ML research community to access and experiment with this data. To increase accessibility and innovation on private insurance data, we compile and share publicly available insurance datasets, …
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Learning distributions of transformations from small datasets for applied image synthesis
… focuses on applications with large labeled datasets. However, in realistic settings, it is much more common to work with limited data. In this thesis, we investigate two applications of image synthesis using small datasets. First, we demonstrate how to use image synthesis to perform data …
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Acoustic-based machine learning diagnostic tool for voice disorders
… as input, and a novel CNN model using data augmentation idea with scalogram of the speech as input. The deep acoustic recurrent model explores the relationship of frame-based cepstral features with RNN model. Two novel cepstral features based on cepstrum are proposed: Second Peak …
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A citizen science approach for the collection of data to train deep learning models
… predictions require a considerable amount of data, which can sometimes be a challenge to collect. Due to the size of the island, the study of Maltese flora is one of such fields that lacks available data causing little technological advancements. Training a deep learning network with lack of …
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