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 109 for “"Domain Adaptation"”.
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Domain adaptation with minimal training
… model trained on labeled data of a (source) domain degrades severely when they are tested on a different (target) domain. Traditional approaches deal with this problem by training a new model for every target domain. In natural language processing, top performing systems often use multiple …
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Federated domain adaptation for healthcare
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01
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DOMAIN ADAPTATION FOR AUTOMATED ESSAY SCORING
… gives an overview of the AES task and shows that domain adaptation can help an AES system to achieve high performance with a small number of annotated essays.
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Domain Adaptation using Deep Adversarial Models
… Traditionally, data sets lie within the same domain and the same distribution is assumed for both training and testing sets. In many real-world scenarios such assumption would lead to very poor results, because data distribution may frequently be similar but not exactly identical. Sometimes, …
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Robust Domain Adaptation Using Active Learning
… One way to mitigate this problem is to use domain adaptation techniques; these techniques build a new model on the unlabeled test dataset (target dataset) by transferring information from a related but labeled training dataset, (source dataset) even when their underlying distributions are …
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Domain Adaptation in Natural Language Processing
Although we focus on domain adaptation in natural language processing in this thesis, most of the analysis of the problem and the proposed domain adaptation techniques are not restricted to natural language processing problems but can be generally applied to most classification tasks when the …
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Domain adaptation for neural machine translation
… struggle when translating text of a specific domain. A domain may consist of text on a well-defined topic, or text of unknown provenance with an identifiable vocabulary distribution, or language with some other stylometric feature. While NMT models can achieve good translation performance on …
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Generative gradual domain adaptation with optimal transport
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01
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Multi-source domain adaptation with mixture of experts
… a mixture-of-experts approach for unsupervised domain adaptation from multiple sources. The key idea is to explicitly capture the relationship between a target example and different source domains. This relationship, expressed by a point-to-set metric, determines how to combine predictors …
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Data Acquisition for Domain Adaptation of Closed-Box Models
… they may suffer from distribution shifts in new domains. Prior techniques cannot address this problem, because they are either impractical to use or against the property of closed-box models. Instead, we propose to acquire extra data to construct a "padding" model to help the original closed box …
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Enhancing knowledge distillation in large language models via domain adaptation
Domain-Adaptive Pre-Training (DAPT) is widely used to improve Large Language Models on specialized domains, yet its interaction with knowledge distillation (KD) remains poorly understood. In particular, intermediate DAPT checkpoints are rarely analyzed, and the evolution of teacher uncertainty …
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Understanding gradual domain adaptation: Improved analysis, optimal path and beyond
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01
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Combating fake news with adversarial domain adaptation and neural models
… are then supplemented with an adversarial domain adaptation technique, which helps the models overcome dataset size limitations. We test the performance of these models by using the Fake News Challenge (FNC) [Pomerleau and Rao, 2017], the Fact Extraction and VERification (FEVER) [Thorne et …
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Domain Adaptation with a Classifier Trained by Robust Pseudo-Labels
… of distribution discrepancies between these domains, directly training the model on the source domain cannot be expected to generate satisfactory results on the target domain. Therefore, the problem of minimizing these data distribution discrepancies is the main challenge with which modern …
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Disaster tweet classification using parts-of-speech tags: a domain adaptation approach
… for a prior source disaster. Therefore, domain adaptation algorithms that make use of labeled data from a source disaster to learn classifiers for the target disaster provide a promising direction in the area of tweet classification for disaster management. In prior work, domain …
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Unsupervised Domain Adaptation per la rilevazione di oggetti e riconoscimento di azioni
Questa tesi affronta il problema di unsupervised domain adaptation (UDA) per la rilevazione degli oggetti e il riconoscimento delle azioni. UDA è una tecnica di machine learning che mira a ridurre le differenze di distribuzione tra un dominio di origine (con dati etichettati) e un dominio di …
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Enhancing Breast Cancer Detection Through Combination of Contrastive Learning and Adversarial Domain Adaptation
… lack of being adaptable to new or different data domains. This thesis uses cutting-edge deep learning methods to address these issues. We use self-supervised learning techniques like Bootstrap Your Own Latent (BYOL) and Simple Framework for Contrastive Learning of Visual Representations (SimCLR) …
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QLoRaX: Heuristic-Guided Fine-Tuning of LLaMA-2 for Domain Adaptation in Entrepreneurship
… fine-tuning of large language models (LLMs) for domain-specific applications using limited data. We fine-tuned the LLaMA-2 (7B) model on a curated entrepreneurial dataset containing 3,545 human-written question-answer pairs, of which 3,095 were used for training and 450 were reserved for …
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Domain Adaptation of LLMs for Materials Science: Dataset Curation, Fine-Tuning, and Evaluation Benchmark
… science remains limited due to the lack of domain-specific natural language datasets and evaluation benchmarks. To overcome this challenge, the thesis introduces a curated instruction-tuning dataset composed of diverse question-answer (QA) pairs drawn from various materials science sources …
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Enhancing Generalization in Sketch-Based Image Retrieval through Single and Multi-Source Domain Adaptation
… generalization framework, the research proposes domain adaptation strategies specifically tailored for SBIR to bridge the significant gap between sketch and image domains . A single-source domain adaptation algorithm is introduced, uti- lizing canonical correlation analysis (CCA) alongside …
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