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 42 for “"Data-Sparsity"”.
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Multi-level acoustic modeling for automatic speech recognition
… examples to train a robust model, resulting in a data sparsity problem. For nearly two decades, this data sparsity problem has been dealt with by a clustering-based framework which systematically groups different context-dependent units into clusters such that each cluster can have enough data. …
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The Unreasonable Usefulness of Approximation by Linear Combination
Through the exploitation of data-sparsity ---a catch all term for savings gained from a variety of approximations--- it is possible to reduce the computational cost of accurate electronic structure calculations to linear. Meaning, that the total time to solution for the calculation grows at the …
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Extracting the Wisdom of Crowds From Crowdsourcing Platforms
… context—without tapping into the vast amount of data available on online crowdsourcing platforms. To extract WoC from participants on online platforms, there exist at least three challenges, including social influence, suboptimal aggregation strategies, and data sparsity. This dissertation aims …
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Bayesian Neural Networks for Actuarial Mortality Modelling
… known for their application to sparse or small datasets. This research investigates the efficacy of BNN architectures in the presence of extreme data sparsity and explores methodologies for incorporating domain-specific prior information through hybrid structures. By embedding classical …
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Learning Morphology for Open-Vocabulary Neural Machine Translation
… word representations under conditions of high data sparsity. This problem is an important bottleneck on performance, especially in morphologically-rich languages, where the word vocabulary tends to be huge and sparse. In this dissertation, we propose to solve the vocabulary limitation problem …
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Using K-means Clustering and Similarity Measure to Deal with Missing Rating in Collaborative Filtering Recommendation Systems
… Filtering approach has its limitation of data sparsity and online scalability problems which result in low recommendation quality. In this thesis, a novel Collaborative Filtering approach is introduced using clustering and similarity technologies. The proposed method using K-means …
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Unsupervised methods for speaker diarization
Given a stream of unlabeled audio data, speaker diarization is the process of determining "who spoke when." We propose a novel approach to solving this problem by taking advantage of the effectiveness of factor analysis as a front-end for extracting speaker-specific features and exploiting the …
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NBA Sleep Tracking Data Imputation
… methods for nights of missing sleep wearable data from NBA Academy athletes. Sparsity in sleep tracking data arises as a result of behavioral non-compliance or device malfunction, hindering the NBA Academy's ability to provide actionable insights that improve player sleep, a crucial component …
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Hybrid recommender systems for personalized government-to-business e-services
… applications, they still suffer from data uncertainty, data sparsity, cold-start item and cold-start user problems, resulting in poor recommendation accuracy and reduced coverage. An effective solution proposed in this research to alleviate such problems is the development of hybrid …
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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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Advancing Deep Learning Efficiency: From Specialized Co-Design to Automated Generation
… improvements through the exploitation of data sparsity. Finally, we open up the hardware design space beyond template-based sizing, and progress into the automated learning-based co-design of neural network and hardware architectures, maximizing their synergy with a full-stack joint …
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A Novel Adaptation Model for E-Learning Recommender Systems Based on Student’s Learning Style
… which solves the issues of cold-start and data sparsity in- herent to the traditional Collaborative Filtering (CF) and the traditional Content-based Filtering (CBF), respectively. Moreover, these two new algorithms have been combined to create a new Enhanced Hybrid Filtering (EHF) algorithm …
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Deep Transfer Learning for Macroscale Defect Detection in Semiconductor Manufacturing
… in detail, and a novel approach to overcoming data scarcity through the creation of synthetic data is deployed. The binary classifier model achieves an out-of-distribution area under curve (AUC) of 0.909 for detecting hotspot defects. Detection for other classes of central defects is also …
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Geo-Informed Deep Learning for Spatial Downscaling of Solute Transport in Heterogeneous Porous Media
… media is a complex phenomena as it encounters a data sparsity challenge when investigated experimentally and a computational cost challenge when simulated numerically. This work proposes a unique two-stage deep learning architecture comprising a dual-branch autoencoder and a Geo-informed …
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Bayesian Modeling of a High Resolution Housing Price Index
… at, for example, the census tract level is the sparsity of spatiotemporally localized house sales observations. Our work addresses the data sparsity challenge by leveraging observations from multiple census tracts discovered to have correlated valuation dynamics. We propose a Bayesian …
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Scalable Bayesian Matrix and Tensor Factorization for Discrete Data
… methods decompose the observed matrix and tensor data into a set of factor matrices. They provide a useful way to extract latent factors or features from complex data, and also to predict missing data. Matrix and tensor factorization has drawn significant attentions in a wide variety of …
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Power To The People - Harnessing Innovative Approaches To Address Hydrologic Challenges
… challenges requiring innovative approaches in data collection, technological solutions, and advanced analytical methods. This dissertation presents four interconnected research papers that advance the field through novel methodologies and practical applications.The research begins by addressing …
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Dynamic marketing policies : constructing Markov states for reinforcement learning
… Markov states from historical transaction data by adapting a method that has been proposed in the computer science literature. Rather than designing states in transaction space, we construct predictions over how customers will respond to a firm's marketing actions. We then design states …
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Machine Learning Methods for Churn Prediction and Infrastructure Resilience
… across various contract tenures. To address data sparsity and longitudinal gaps in newer contract types, I employ synthetic data generation and imputation techniques, such as regression-based methods and Multivariate Imputation by Chained Equations (MICE). For forecasting churn and upgrade …
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Architecture design for highly flexible and energy-efficient deep neural network accelerators
… Eyeriss features a novel Row-Stationary (RS) dataflow to minimize data movement when processing a DNN, which is the bottleneck of both performance and energy efficiency. The RS dataflow supports highly-parallel processing while fully exploiting data reuse in a multi-level memory hierarchy to …
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