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 68 for “"Weakly-supervised"”.
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Weakly-supervised text classification
… many real-world applications. Although many semi-supervised and weakly-supervised text classification models exist, they cannot be easily applied to deep neural models and meanwhile support limited supervision types. In this work, we propose a weakly-supervised framework that addresses the lack of …
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Weakly Supervised Machine Learning for Cyberbullying Detection
… deep ensembles outperform non-deep methods for weakly supervised harassment detection. For the second goal, we geared this research toward a very important topic in any online automated harassment detection: fairness against particular targeted groups including race, gender, religion, and sexual …
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Weakly supervised text mining with text-rich networks
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms
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Weakly supervised aspect extraction for domain-specific texts
Aspect extraction, identifying aspects of text segments from a pre-defined set of aspects, is one of the keystones in text understanding. It benefits numerous applications, including sentiment analysis and product review summarization. Most existing aspect extraction methods heavily rely on …
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Weakly supervised learning from referring expression: Challenge and directions
We explore methods of weakly supervised learning from referring expression. Unlike traditional fully supervised semantic segmentation of object recognition tasks, in which a a small set of discrete class bases is provided, the referring expression task is performed associated with a sentence …
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Weakly Supervised Representation Learning for Trauma Injury Pattern Discovery
… with a disentangled variational autoencoder, weakly supervised by a latent-space classifier of auxiliary features. We also develop a novel scoring metric that serves as a proxy for clinical intuition in extracting clusters with clinically meaningful injury patterns. We validate the extracted …
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Knowledge-Informed Weakly-Supervised Deep Learning Models for Cancer Applications
… in cancer imaging. They combine self-supervised pretraining, knowledge-informed loss functions, hierarchical and contextualized architectures, and label smoothing techniques that distill clinical and biological priors. These approaches enable dense spatial prediction of gene modules, …
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Robust Deep Learning Methodologies for Weakly Supervised Remote Sensing Image Classification
… by developing a suite of novel methodologies for weakly supervised learning (WSL) in RS with a focus on classification tasks such as land-cover mapping and scene classification. WSL strategies are commonly subdivided into three different categories: i) inaccurate supervision, which deals with …
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Weakly-supervised traversability estimation for mobile robots using sparse point annotation
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms
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Mining social media stimulus from news article text using weakly-supervised narrative classification
… dataset, which stops us from using existing weakly supervised text classification methods that heavily depend on class name semantics. 3) The noisy news article dataset: the collected dataset does not guarantee the documents will belong to any of the narratives. In such cases, the power of …
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A study of remote sensing based natural and built environment monitoring: from fully supervised to weakly supervised learning
… advantages and usage by employing the classical supervised learning methodology. Through this practice, the operational readiness of adopting optical RS technologies for flood disaster management is analyzed, and technical challenges are identified. In addition, I fill a technical gap in past RS …
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Learning with Constraint-Based Weak Supervision
… for quality training data. Typically, training supervised machine learning systems involves using large amounts of human-annotated data. Labeling data is expensive and can be a limiting factor in using machine learning models. To enable continued integration of machine learning systems in …
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Automated structuring of text space with minimal supervision
… this gap, this dissertation aims to develop weakly supervised methods to structure the text space in a multi-granular and multi-aspect way. To accomplish this goal, the following tasks are studied. 1. Taxonomy Construction and Enrichment. Constructing a hierarchical representation of …
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Learning Deep Visual Features from Limited Labeled Data
… to learn general visual features including semi-supervised methods which learn visual features from a small size of labeled data and a large amount of unlabeled data, weakly supervised methods which learn visual features from coarse-grained labeled data, and self-supervised methods which learn …
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SnapshotNet: Self-supervised Feature Learning for Point Cloud Data Segmentation Using Minimal Labeled Data
… model called SnapshotNet is proposed as a self-supervised feature learning approach, which directly works on the unlabeled point cloud data of a complex 3D scene. The SnapshotNet pipeline includes three stages. In the snapshot capturing stage, snapshots, which are defined as local collections of …
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Deep heterogeneous superpixel neural networks for image analysis and feature extraction
… achieve higher-level object relation modeling, weakly supervised segmentation, high explainability, and facilitate insightful visualizations. This approach has the advantage of being an efficient representation of the visual signal and has the capability to dissect out relevant object components …
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Groundtruth budgeting : a novel approach to semi-supervised relation extraction in medical language
We address the problem of weakly-supervised relation extraction in hospital discharge summaries. Sentences with pre-identified concept types (for example: medication, test, problem, symptom) are labeled with the relationship between the concepts. We present a novel technique for weakly-supervised …
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Visual feature learning with application to medical image classification
… approaches to learn local features using unsupervised and weakly-supervised methods, and an approach to improve the feature encoding methods such as bag-of-words. Unlike the existing work, the proposed weakly-supervised approach uses image-level labels to learn the local features. Requiring …
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Text Prompt-Driven Medical Image Segmentation
… multimodal segmentation methods targeting fully supervised and weakly supervised settings, with each method addressing specific bottlenecks. Our main contribution lies in the use of text-driven guidance to solve the respective challenges: computational inefficiency in full supervision and …
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Learning without Expert Labels for Multimodal Data
… for learning with indirect supervision include unsupervised learning, self-supervised learning, weakly supervised learning, few-shot learning, and knowledge distillation. This thesis addresses these opportunities in the context of multi-modal data through three main contributions. First, this …
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