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 6 of 6 for “"graph-based learning"”.
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Towards Network-Guided Large-Scale Foundation Models on Single-Cell Transcriptomics
… NLP and computer vision. Recently, transformer-based foundation models tailored for single-cell RNA sequencing (scRNA-seq) data have shown significant potential in interpreting the 'languages' of cells through self-supervised learning on huge amounts of unlabeled scRNA-seq datasets. These models …
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Minimal Labels, Maximum Gain. Image Classification with Graph-Based Semi-Supervised Learning
… last decade, the use and deployment of machine learning systems for computer vision has risen dramatically. To train a machine learning model it is often assumed that the practitioner has access to a large and representative labelled dataset from which they can optimise their model in a …
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Trust models for mobile content-sharing applications
… more accurate than existing approaches by using graph-based learning. 4. An algorithm that learns the similarity between any two categories by extracting similarities between the two categories’ ratings rather than by requiring a universal ontology. It does so automatically by using Singular …
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Graph-based approaches for semi-supervised and cross-domain sentiment analysis
… binary and multiclass sentiment scales. We adopt graph-based learning as our main method and explore the most popular and widely used graph-based algorithm, label propagation. We investigate various ways of designing sentiment graphs and propose a new similarity measure which is unsupervised, easy …
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Online and active learning of big networks: theory and algorithms
… performance. In particular, I present active learning, online learning, selective sampling (online active learning), and online learning with bandit feedback algorithms for learning in a network. In the first part of this thesis, I propose a \textit{nonadaptive} active learning approach on a …
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Large-Scale Machine Learning for Classification and Search
… billions, can be collected for training machine learning models. Inspired by this trend, this thesis is dedicated to developing large-scale machine learning techniques for the purpose of making classification and nearest neighbor search practical on gigantic databases. Our first approach is to …