Zobrazeno 1 - 10
of 14 430
pro vyhledávání: '"P Kelleher"'
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 knowledge graphs, Knowledge Graph Embeddings (KGEs) have been developed
Externí odkaz:
http://arxiv.org/abs/2412.14801
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:
Kelleher, Colm
Density matrices are the most general descriptions of quantum states, covering both pure and mixed states. Positive semidefiniteness is a physical requirement of density matrices, imposing nonnegative probabilities of measuring physical values. Separ
Externí odkaz:
http://arxiv.org/abs/2407.19061
We introduce and describe a new heuristic method for finding an upper bound on the degree of contextuality and the corresponding unsatisfied part of a quantum contextual configuration with three-element contexts (i.e., lines) located in a multi-qubit
Externí odkaz:
http://arxiv.org/abs/2407.02928
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:
Liu, Yifan, Jin, Naijun, Lee, Dahyeon, McLemore, Charles, Nakamura, Takuma, Kelleher, Megan, Cheng, Haotian, Schima, Susan, Hoghooghi, Nazanin, Diddams, Scott, Rakich, Peter, Quinlan, Franklyn
We demonstrate a vacuum-gap ultrastable optical reference cavity that does not require a vacuum enclosure. Our simple method of optical contact bonding in a vacuum environment allows for cavity operation in air while maintaining vacuum between the ca
Externí odkaz:
http://arxiv.org/abs/2406.13159
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
Autor:
Kelleher, Ruth, Lelli, Federico
We study the mass distribution of galaxy clusters in Milgromian dynamics, or modified Newtonian dynamics (MOND). We focus on five galaxy clusters from the X-COP sample, for which high-quality data are available on both the baryonic mass distribution
Externí odkaz:
http://arxiv.org/abs/2405.08557