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 249 for “"Fine-tuned"”.
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Self-organized fine-tuned response in a driven spin glass
In this thesis, I investigate the principles that that can be used to predict the behavior of a many-bodied system when an external drive is applied. I consider a spin glass as a prototypical model of such a system, and investigate these principles through simulation. I find that spins …
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Deep learning-based fine-tuned multi-vehicle tracking with classification correction
… optimization. To perform vehicle tracking, a fine-tuned classification dataset was created from multiple sources. A ResNet classifier was trained, and its knowledge was utilized to build a localization dataset automatically without manual annotations. The dataset consists of videos generated …
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Automated Synthesis Procedure Generation in Heterogeneous Catalysis via Fine-Tuned Language Models
… Subsequently, this structured information was refined through rigorous classification, synthesis query generation, and feasibility validation, resulting in a curated dataset comprising 1,632 high-quality catalyst synthesis procedures. Leveraging this dataset, we fine-tuned a large language model …
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A retrieval augmented generation fine-tuned LLM model for refactored code recommendations to mitigate Java lock contention performance faults
… Retrieval Augmented Generation (RAG) with a fine-tuned LLM model for refactored code recommendation aimed at reducing lock-contention performance faults in Java applications. The RAG-based fine-tuned model combines the strengths of contextual understanding from LLMs with the precision of …
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Fine-tuning a domain-specific language model for truss structural analysis
This research investigates the feasibility of fine-tuning a domain-specific vison-language and large-language for truss structural analysis. General-purpose AI models often struggle with engineering-specific problems due to insufficient domain knowledge. To address this, we propose a hybrid …
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Generative Models for Domain-Specific Summarization
… analyzed in both their zero-shot learning and fine-tuned performance against state-of-the-art models. In zero-shot learning, generative models were superior in most cases to the state-of-the- art models, whereas the fine-tuned models could learn with less information about the dataset. These …
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An image-text model for effective retrieval of seventeenth-century Spanish American notary records
… and text through high-dimensional embeddings. We fine-tuned the CLIP model using a specialized dataset of handwritten Spanish words, curated and annotated by paleography experts, focusing on maximizing the alignment between image and text embeddings. This fine-tuning involved adjusting the model …
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Adapting Large-Scale Speaker-Independent Automatic Speech Recognition to Dysarthric Speech
… hours of standard speech could successfully be fine-tuned to dysarthric speech. Deep Speech – an open-source Deep Learning based speech recognition system developed by Mozilla – was used as the baseline model. The UASpeech dataset, composed of utterances from 15 speakers with cerebral palsy, was …
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Radiology Report Summarization With Large Language Models
… histories, this thesis proposes a two-stage fine-tuning strategy for radiology report summarization using both decoder-only and encoder-decoder large language models (LLMs), specifically LLaMa 3.1-8B-Instruct and T5-Base. In the first stage, we fine-tune each model on MIMIC-CXR, a large …
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CLIP-RS: A Cross-modal Remote Sensing Image Retrieval Based on CLIP, a Northern Virginia Case Study
… scarcities limit the scope of fine tuned state of the art models to at most 38 classes, based on the PatternNet data, one of the largest publicly available labeled RS data. Recent state-of-the art image-to-image retrieval and detection models in RS have shown great results. …
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Impacts of LLM-Based Text Normalization on Price Prediction
… the impact across three modeling pipelines: a fine-tuned DistilBERT encoder with a linear regression head, a frozen DistilBERT encoder with a linear regression head, and a frozen DistilBERT encoder with an XGBoost regressor. All three pipelines show modest improvement after normalization, with …
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A question to query LLM as a pipeline replacement in knowledge graph question answering systems
… a drop-in replacement for those modules: a fine-tuned large language model (LLM) that translates a natural-language question directly into an executable SPARQL query. We fine-tune instruction-tuned backbones, Llama-3.1-8B-Instruct and Mistral-7B-Instruct, on paired (question, gold SPARQL) …
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Studying The Effectiveness Of Large Language Models In Benchmark Biomedical Tasks
… current state-of-the-art models when they were fine-tuned only on the training set of these datasets. This suggests that pretraining on large text corpora makes LLMs quite specialized even in the biomedical domain. The findings also shows that not a single LLM can outperform other LLMs in all …
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Toward Global Localization of Unmanned Aircraft Systems using Overhead Image Registration with Deep Learning Convolutional Neural Networks
… and a non-handcrafted feature-based pre-trained fine-tuned VGG-16 DLCNN on coarse-grained image registration. Both handcrafted and non-handcrafted feature based methods had difficulty with the coarse-grained registration process. The format of JNE RCI is determined to be unsuitable for the …
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Fine-tuning CLIP models for peripheral blood cell images retrieval based on morphological descriptions
This master's thesis explores the fine-tuning of Contrastive Language-Image Pre-training (CLIP) models for retrieving peripheral blood cell images based on morphological descriptions, aiming to assist hematologists in diagnostic processes. The study addresses the limitations of manual peripheral …
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Predicting Patient Outcomes in the EPOCH Clinical Trial
… model pre-trained on liver CT and MRI scans and fine-tuned to predict treatment response resulted in a substantially higher C-index. Interestingly, models fine-tuned to predict one clinical feature had improved predictive accuracy for other features they were not specifically trained on, and …
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Sentiment Analysis with Language Models on Finnish Workplace Well-Being Surveys
… embeddings, generative language methods, and fine-tuned BERT models. To our knowledge, this is the first study evaluating the use of language models on the Finnish sentiment analysis task. Additionally, we evaluated the feasibility of implementing these methods based on their operating costs …
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QLoRaX: Heuristic-Guided Fine-Tuning of LLaMA-2 for Domain Adaptation in Entrepreneurship
<p>This thesis presents a study on the 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 …
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You Only Look Twice: An Ensemble Deep Learning Model for Wildfire Detection Using Terrestrial Camera Networks
… YOL2, a novel ensemble approach that combines a fine-tuned ConvNeXt Convolutional Neural Network incorporating a Dynamic Tanh normalization layer with a fine-tuned YOLO11 model for precise localization. Using a comprehensive dataset of 33,636 time-sequenced images from terrestrial cameras across …
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