Sökning: "Zero-Shot Learning"

Visar resultat 1 - 5 av 25 uppsatser innehållade orden Zero-Shot Learning.

1. 1. Bridging Language & Data : Optimizing Text-to-SQL Generation in Large Language Models

   Master-uppsats, Linköpings universitet/Artificiell intelligens och integrerade datorsystem

   Författare :Niklas Wretblad; Fredrik Gordh Riseby; [2024]
   Nyckelord :Chaining; Classification; Data Quality; Few-Shot Learning; Large Language Model; Machine Learning; Noise; Prompt; Prompt Engineering; SQL; Structured Query Language; Text-to-SQL; Zero-Shot Learning; Noise Identification;

   Sammanfattning : 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. LÄS MER

3. 2. Topological regularization and relative latent representations

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Alejandro García Castellanos; [2023]
   Nyckelord :Algebraic Topology; Large Language Models; Relative Representation; Representation Learning; Model Stitching; Topological DataAnalysis; Zero-shot; Algebraisk topologi; Stora språkmodeller; Relativ representation; Representationsinlärning; Modell sömmar; Topologisk dataanalys; Zero-shot;

   Sammanfattning : This Master's Thesis delves into the application of topological regularization techniques and relative latent representations within the realm of zero-shot model stitching. Building upon the prior work of Moschella et al. LÄS MER

4. 3. Anomaly Detection with Machine Learning using CLIP in a Video Surveillance Context

   Master-uppsats, Linköpings universitet/Datorseende

   Författare :Christoffer Gärdin; [2023]
   Nyckelord :Datorseende; maskininlärning; CLIP; anomalidetektion; videoövervakning;

   Sammanfattning : This thesis explores the application of Contrastive Language-Image Pre-Training (CLIP), a vision-language model, in an automated video surveillance system for anomaly detection. The ability of CLIP to perform zero-shot learning, coupled with its robustness against minor image alterations due to its lack of reliance on pixel-level image analysis, makes it a suitable candidate for this application. LÄS MER

5. 4. Generation of Synthetic Traffic Sign Images using Diffusion Models

   Master-uppsats, Linköpings universitet/Datorseende

   Författare :Johanna Carlson; Lovisa Byman; [2023]
   Nyckelord :Machine Learning; Computer Vision; Diffusion Models; Traffic Sign Recognition; Traffic Sign Classification; Synthetic Data; Maskininlärning; Datorseende; Diffusionsmodeller; Trafikskyltsigenkänning; Trafikskyltsklassificering; Syntetisk data;

   Sammanfattning : 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. LÄS MER

7. 5. Prompt-learning and Zero-shot Text Classification with Domain-specific Textual Data

   Master-uppsats, Uppsala universitet/Institutionen för lingvistik och filologi

   Författare :Hengyu Luo; [2023]
   Nyckelord :prompt-learning; zero-shot; few-shot; text classification; domain-specific; retail sector domain; customer-agent interaction; transformer; large language models; ChatGPT; natural language processing; machine learning; deep learning;

   Sammanfattning : 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. LÄS MER