6 papers
Active Learning and Transfer Learning for Anomaly Detection in Time-Series Data
John D. Kelleher, Matthew Nicholson, Rahul Agrahari +1
This paper examines the effectiveness of combining active learning and transfer learning for anomaly detection in cross-domain time-series data. Our results indicate that there is…
Extending TWIG: Zero-Shot Predictive Hyperparameter Selection for KGEs based on Graph Structure
Jeffrey Sardina, John D. Kelleher, Declan O'Sullivan
Knowledge Graphs (KGs) have seen increasing use across various domains -- from biomedicine and linguistics to general knowledge modelling. In order to facilitate the analysis of kn…
A Survey on Knowledge Graph Structure and Knowledge Graph Embeddings
Jeffrey Sardina, John D. Kelleher, Declan O'Sullivan
Knowledge Graphs (KGs) and their machine learning counterpart, Knowledge Graph Embedding Models (KGEMs), have seen ever-increasing use in a wide variety of academic and applied set…
BIS: NL2SQL Service Evaluation Benchmark for Business Intelligence Scenarios
Bora Caglayan, Mingxue Wang, John D. Kelleher +4
NL2SQL (Natural Language to Structured Query Language) transformation has seen wide adoption in Business Intelligence (BI) applications in recent years. However, existing NL2SQL be…
A framework for measuring the training efficiency of a neural architecture
Eduardo Cueto-Mendoza, John D. Kelleher
Measuring Efficiency in neural network system development is an open research problem. This paper presents an experimental framework to measure the training efficiency of a neural…
TWIG: Towards pre-hoc Hyperparameter Optimisation and Cross-Graph Generalisation via Simulated KGE Models
Jeffrey Sardina, John D. Kelleher, Declan O'Sullivan
In this paper we introduce TWIG (Topologically-Weighted Intelligence Generation), a novel, embedding-free paradigm for simulating the output of KGEs that uses a tiny fraction of th…