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 175 for “"structured data"”.
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Information overload in structured data
… information overload problem in two separate structured domains, namely, graphs and text.</p> <p>Graph kernels have been proposed as an efficient and theoretically sound approach to compute graph similarity. They decompose graphs into certain sub-structures, such as subtrees, or subgraphs. …
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Deep learning and structured data
In the recent years deep learning has witnessed successful applications in many different domains such as visual object recognition, detection and segmentation, automatic speech recognition, natural language processing, and reinforcement learning. In this thesis, we will investigate deep learning …
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Techniques for structured data discovery
The discovery of structured data, or data that is tagged by key-value pairs, is a problem that can be subdivided into two issues: how best to structure information architecture and user interaction for discovery; and how to intelligently display data in a way that that optimizes the discovery of …
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Efficient Similarity Search in Structured Data
Modern database applications are characterized by two major aspects: the use of complex data types with internal structure and the need for new data analysis methods. The focus of database users has shifted from simple queries to complex analyses of the data, known as knowledge discovery in …
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Kernel Methods for Tree Structured Data
… information from large collections of noisy data. In many real world applications, data is naturally represented in structured form. Since traditional methods in machine learning deal with vectorial information, they require an a priori form of preprocessing. Among all the learning techniques …
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Adapting Transformers for Structured Data Domains
… and effectiveness of Transformers in structured data domains beyond their traditional use in natural language processing (NLP). We revisit key elements of the transformer framework - including input representations, attention formulations, auxiliary tasks, prediction layers and loss …
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Scaling Multidimensional Inference for Big Structured Data
<p>"In information technology, big data is a collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications" [151]. In a</p><p>world of increasing sensor modalities, cheaper storage, and more data oriented questions, we are quickly …
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Focus-based Interactive Visualization for Structured Data
… that studies visual representations of abstract data where no spatial representation is available, has been playing an essential role in assisting people to understand the vast amount of information created by modern technology. Visualizing large complex structured data is an important area as …
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Learning from Structured Data with Weak Supervision
… to test them, and collect and interpret data. Fundamental advances over the past decade include self-supervised learning methods that train models on broad data at scale without pre-defined labels, geometric deep learning that leverages structure and geometry informed by scientific …
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Towards Scalable Structured Data from Clinical Text
… many pertinent variables are trapped in unstructured clinical note text. Automated extraction is difficult since clinical notes are written in their own jargon-heavy dialect, patient histories can contain hundreds of notes, and there is often minimal labeled data. In this thesis, I tackle …
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Modeling Structured Data with Invertible Generative Models
Data is complex and has a variety of structures and formats. Modeling datasets is a core problem in modern artificial intelligence. Generative models are machine learning models, which model datasets with probability distributions. Deep generative models combine deep learning with probability …
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Algorithms on graph-structured data with imperfect information
Graph-structured data is able to characterize pairwise or even higher-order relations among different data points, and has been demonstrated to be highly advantageous in various data mining and machine learning applications. Such graph-structured data may either come from real life networks, or …
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Semi-automatic matching of semi-structured data updates
Data matching, also referred to as data linkage or field matching, is a technique used to combine multiple data sources into one data set. Data matching is used for data integration in a number of sectors and industries; from politics and health care to scientific applications. The motivation for …
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Automated extraction of structured data from HTML documents
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.
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Fusion Tables : new ways to collaborate on structured data
Fusion Tables allows data collaborators to create, merge, navigate and set access control permissions on structured data. This thesis focuses on the collaboration tools that were added to Googles Fusion Tables. The collaboration tools provided additional functionality: first, the ability to view, …
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Modeling tools for the integration of structured data sources
Disparity in representations within structured documents such as XML or SQL makes interoperability challenging, error-prone and expensive. A model is developed to process disparate representations to an encompassing generic knowledge representation. Data sources were characterized according to a …
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Towards Efficient and Scalable Deep Learning on Graph-Structured Data
… Effective and Scalable Deep Learning on Graph-Structured Data," proposes novel methodologies to address these limitations across four main research thrusts. To address scalability in learning node embeddings, one paper introduces CCA-SSG, a self-supervised framework that learns robust node …
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A generalization based hybrid algorithm for clustering semi-structured data
… construction, object generalization and data clustering is presented. The algorithm works well on semi-structured data and requires only a minimum of domain knowledge. Since the algorithm reduces the dimensionality of the semi-structured data, clustering of the resulting generalized data …
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