Zobrazeno 1 - 10
of 142
pro vyhledávání: '"Lawlor, Aonghus"'
Autor:
Liu, Dairui, Du, Honghui, Yang, Boming, Hurley, Neil, Lawlor, Aonghus, Li, Irene, Greene, Derek, Dong, Ruihai
Pre-trained transformer models have shown great promise in various natural language processing tasks, including personalized news recommendations. To harness the power of these models, we introduce Transformers4NewsRec, a new Python framework built o
Externí odkaz:
http://arxiv.org/abs/2410.13125
The diagnostic accuracy and subjectivity of existing Knee Osteoarthritis (OA) ordinal grading systems has been a subject of on-going debate and concern. Existing automated solutions are trained to emulate these imperfect systems, whilst also being re
Externí odkaz:
http://arxiv.org/abs/2407.11500
Publikováno v:
Thirty-seventh Conference on Neural Information Processing Systems Workshop on Medical Imaging meets NeurIPS 2023
Knee Osteoarthritis (OA) is a debilitating disease affecting over 250 million people worldwide. Currently, radiologists grade the severity of OA on an ordinal scale from zero to four using the Kellgren-Lawrence (KL) system. Recent studies have raised
Externí odkaz:
http://arxiv.org/abs/2407.09515
Active learning (AL) has found wide applications in medical image segmentation, aiming to alleviate the annotation workload and enhance performance. Conventional uncertainty-based AL methods, such as entropy and Bayesian, often rely on an aggregate o
Externí odkaz:
http://arxiv.org/abs/2401.16298
Autor:
Liu, Dairui, Yang, Boming, Du, Honghui, Greene, Derek, Hurley, Neil, Lawlor, Aonghus, Dong, Ruihai, Li, Irene
News recommendations heavily rely on Natural Language Processing (NLP) methods to analyze, understand, and categorize content, enabling personalized suggestions based on user interests and reading behaviors. Large Language Models (LLMs) like GPT-4 ha
Externí odkaz:
http://arxiv.org/abs/2312.10463
Autor:
Du, Haiwen, Ju, Zheng, An, Yu, Du, Honghui, Zhu, Dongjie, Tian, Zhaoshuo, Lawlor, Aonghus, Dong, Ruihai
Spectrum analysis systems in online water quality testing are designed to detect types and concentrations of pollutants and enable regulatory agencies to respond promptly to pollution incidents. However, spectral data-based testing devices suffer fro
Externí odkaz:
http://arxiv.org/abs/2308.01138
Publikováno v:
Pacific-Asia Conference on Knowledge Discovery and Data Mining 2023
The use of graph convolution in the development of recommender system algorithms has recently achieved state-of-the-art results in the collaborative filtering task (CF). While it has been demonstrated that the graph convolution operation is connected
Externí odkaz:
http://arxiv.org/abs/2305.18374
Autor:
D'Amico, Edoardo, Muhammad, Khalil, Tragos, Elias, Smyth, Barry, Hurley, Neil, Lawlor, Aonghus
Publikováno v:
LNCS,volume 13980, pp 249-263, 2023
Graph Convolutional Networks (GCN) have been recently employed as core component in the construction of recommender system algorithms, interpreting user-item interactions as the edges of a bipartite graph. However, in the absence of side information,
Externí odkaz:
http://arxiv.org/abs/2303.15946
Recent Anomaly Detection techniques have progressed the field considerably but at the cost of increasingly complex training pipelines. Such techniques require large amounts of training data, resulting in computationally expensive algorithms that are
Externí odkaz:
http://arxiv.org/abs/2301.06957
Publikováno v:
In Computerized Medical Imaging and Graphics July 2024 115
