Essays about: "machine learning language"
Showing result 1 - 5 of 369 essays containing the words machine learning language.
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1. Towards Automated Log Message Embeddings for Anomaly Detection
University essay from Lunds universitet/Institutionen för reglerteknikAbstract : Log messages are implemented by developers to record important runtime information about a system. For that reason, system logs can provide insight into the state and health of a system and potentially be used to anticipate and discover errors. READ MORE
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2. An In-Depth study on the Utilization of Large Language Models for Test Case Generation
University essay from Umeå universitet/Institutionen för datavetenskapAbstract : This study investigates the utilization of Large Language Models for Test Case Generation. The study uses the Large Language model and Embedding model provided by Llama, specifically Llama2 of size 7B, to generate test cases given a defined input. READ MORE
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3. Self-Supervised Learning for Tabular Data: Analysing VIME and introducing Mix Encoder
University essay from Lunds universitet/Fysiska institutionenAbstract : We introduce Mix Encoder, a novel self-supervised learning framework for deep tabular data models based on Mixup [1]. Mix Encoder uses linear interpolations of samples with associated pretext tasks to form useful pre-trained representations. READ MORE
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4. 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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5. Approximating Reasoning with Transformer Language Models
University essay from Göteborgs universitet/Institutionen för data- och informationsteknikAbstract : We conduct experiments with BART, a generative language-model architecture, to investigate its capabilities for approximating reasoning by learning from data. For this we use the SimpleLogic dataset, a dataset of satisfiability problems in propositional logic originally created by Zhang et al. (2022). READ MORE
