Essays about: "Few- Shot Learning"
Showing result 1 - 5 of 30 essays containing the words Few- Shot Learning.
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1. Bridging Language & Data : Optimizing Text-to-SQL Generation in Large Language Models
University essay from Linköpings universitet/Artificiell intelligens och integrerade datorsystemAbstract : This thesis explores text-to-SQL generation using Large Language Models within a financial context, aiming to assess the efficacy of current benchmarks and techniques. The central investigation revolves around the accuracy of the BIRD-Bench benchmark and the applicability of text-to-SQL models in real-world scenarios. READ MORE
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2. Few-Shot Learning for Quality Inspection
University essay from Högskolan i Halmstad/Akademin för informationsteknologiAbstract : The goal of this project is to find a suitable Few-Shot Learning (FSL) model that can be used in a fault detection system for use in an industrial setting. A dataset of Printed Circuit Board (PCB) images has been created to train different FSL models. READ MORE
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3. Evaluating and Fine-Tuning a Few-Shot Model for Transcription of Historical Ciphers
University essay from Uppsala universitet/Institutionen för lingvistik och filologiAbstract : Thousands of historical ciphers, encrypted manuscripts, are stored in archives across Europe. Historical cryptology is the research field concerned with studying these manuscripts - combining the interest of humanistic fields with methods of cryptography and computational linguistics. READ MORE
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4. Generation of Synthetic Traffic Sign Images using Diffusion Models
University essay from Linköpings universitet/DatorseendeAbstract : In the area of Traffic Sign Recognition (TSR), deep learning models are trained to detect and classify images of traffic signs. The amount of data available to train these models is often limited, and collecting more data is time-consuming and expensive. READ MORE
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5. Prompt-learning and Zero-shot Text Classification with Domain-specific Textual Data
University essay from Uppsala universitet/Institutionen för lingvistik och filologiAbstract : The rapid growth of textual data in the digital age presents unique challenges in domain-specific text classification, particularly the scarcity of labeled data for many applications, due to expensive cost of manual labeling work. In this thesis, we explore the applicability of prompt-learning method, which is well-known for being suitable in few-shot scenarios and much less data-consuming, as an emerging alternative to traditional fine-tuning methods, for domain-specific text classification in the context of customer-agent interactions in the retail sector. READ MORE
