13 citations · 15 across the 7 of their papers we have counts for
7 papers
Spam-T5: Benchmarking Large Language Models for Few-Shot Email Spam Detection
Maxime Labonne, Sean Moran
This paper investigates the effectiveness of large language models (LLMs) in email spam detection by comparing prominent models from three distinct families: BERT-like, Sentence Tr…
Estimating class separability of text embeddings with persistent homology
Kostis Gourgoulias, Najah Ghalyan, Maxime Labonne +3
This paper introduces an unsupervised method to estimate the class separability of text datasets from a topological point of view. Using persistent homology, we demonstrate how tra…
A Benchmark Generative Probabilistic Model for Weak Supervised Learning
Georgios Papadopoulos, Fran Silavong, Sean Moran
Finding relevant and high-quality datasets to train machine learning models is a major bottleneck for practitioners. Furthermore, to address ambitious real-world use-cases there is…
Learning a Consensus Sub-Network with Polarization Regularization and One Pass Training
Xiaoying Zhi, Varun Babbar, Rundong Liu +4
The subject of green AI has been gaining attention within the deep learning community given the recent trend of ever larger and more complex neural network models. Existing solutio…
API-Miner: an API-to-API Specification Recommendation Engine
Sae Young Moon, Gregor Kerr, Fran Silavong +1
When designing a new API for a large project, developers need to make smart design choices so that their code base can grow sustainably. To ensure that new API components are well…
Code Librarian: A Software Package Recommendation System
Lili Tao, Alexandru-Petre Cazan, Senad Ibraimoski +1
The use of packaged libraries can significantly shorten the software development cycle by improving the quality and readability of code. In this paper, we present a recommendation…
Topical: Learning Repository Embeddings from Source Code using Attention
Agathe Lherondelle, Varun Babbar, Yash Satsangi +3
This paper presents Topical, a novel deep neural network for repository level embeddings. Existing methods, reliant on natural language documentation or naive aggregation technique…