Search Results for author:
Found 5 papers, 1 papers with code
Estimating Class Separability of Datasets Using Persistent Homology with Application to LLM Fine-Tuning
This paper proposes a method to estimate the class separability of an unlabeled text dataset by inspecting the topological characteristics of sentence-transformer embeddings of the text.
Spam-T5: Benchmarking Large Language Models for Few-Shot Email Spam Detection
Our results demonstrate that Spam-T5 surpasses baseline models and other LLMs in the majority of scenarios, particularly when there are a limited number of training samples available.
Predicting Bandwidth Utilization on Network Links Using Machine Learning
Predicting the bandwidth utilization on network links can be extremely useful for detecting congestion in order to correct them before they occur.
Toward Formal Data Set Verification for Building Effective Machine Learning Models
To guarantee a proper data collection, verifying that the collected data set holds certain properties is a possible solution.
Short-Term Flow-Based Bandwidth Forecasting using Machine Learning
Predictions can be fed to the management system instead of current flows bandwidth in order to take decisions on a more accurate network state.
