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pro vyhledávání: '"Kelleher, John A."'
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 settings. In particular, KGEMs are typically applied to KGs to solve the
Externí odkaz:
http://arxiv.org/abs/2412.10092
Autor:
Caglayan, Bora, Wang, Mingxue, Kelleher, John D., Fei, Shen, Tong, Gui, Ding, Jiandong, Zhang, Puchao
NL2SQL (Natural Language to Structured Query Language) transformation has seen wide adoption in Business Intelligence (BI) applications in recent years. However, existing NL2SQL benchmarks are not suitable for production BI scenarios, as they are not
Externí odkaz:
http://arxiv.org/abs/2410.22925
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 architecture. To demonstrate our approach, we analyze the training eff
Externí odkaz:
http://arxiv.org/abs/2409.07925
Autor:
Abbas, Ammar N., Mehak, Shakra, Chasparis, Georgios C., Kelleher, John D., Guilfoyle, Michael, Leva, Maria Chiara, Ramasubramanian, Aswin K
This study presents a novel methodology incorporating safety constraints into a robotic simulation during the training of deep reinforcement learning (DRL). The framework integrates specific parts of the safety requirements, such as velocity constrai
Externí odkaz:
http://arxiv.org/abs/2407.02231
Autor:
Hunter, Elizabeth, Kelleher, John D.
Stroke is one of the leading causes of death and disability worldwide but it is believed to be highly preventable. The majority of stroke prevention focuses on targeting high-risk individuals but its is important to understand how the targeting of hi
Externí odkaz:
http://arxiv.org/abs/2405.19934
Mass customization and shorter manufacturing cycles are becoming more important among small and medium-sized companies. However, classical industrial robots struggle to cope with product variation and dynamic environments. In this paper, we present C
Externí odkaz:
http://arxiv.org/abs/2404.05870
Transformer-based Neural Language Models achieve state-of-the-art performance on various natural language processing tasks. However, an open question is the extent to which these models rely on word-order/syntactic or word co-occurrence/topic-based i
Externí odkaz:
http://arxiv.org/abs/2403.02009
Autor:
Abbas, Ammar N., Amazu, Chidera W., Mietkiewicz, Joseph, Briwa, Houda, Perez, Andres Alonzo, Baldissone, Gabriele, Demichela, Micaela, Chasparis, Georgios G., Kelleher, John D., Leva, Maria Chiara
Publikováno v:
International Journal of Human-Computer Interaction, 2024
In complex industrial and chemical process control rooms, effective decision-making is crucial for safety and efficiency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an imp
Externí odkaz:
http://arxiv.org/abs/2402.13219
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 the parameters. TWIG learns weights from inputs that consist of topologi
Externí odkaz:
http://arxiv.org/abs/2402.06097
Publikováno v:
Data & Knowledge Engineering, 2023
The difficulty of identifying the physical model of complex systems has led to exploring methods that do not rely on such complex modeling of the systems. Deep reinforcement learning has been the pioneer for solving this problem without the need for
Externí odkaz:
http://arxiv.org/abs/2310.18811