Zobrazeno 1 - 10
of 6 596
pro vyhledávání: '"A, Wijeratne"'
Disease progression models infer group-level temporal trajectories of change in patients' features as a chronic degenerative condition plays out. They provide unique insight into disease biology and staging systems with individual-level clinical util
Externí odkaz:
http://arxiv.org/abs/2410.14388
Sparse Matricized Tensor Times Khatri-Rao Product (spMTTKRP) is the bottleneck kernel of sparse tensor decomposition. In this work, we propose a GPU-based algorithm design to address the key challenges in accelerating spMTTKRP computation, including
Externí odkaz:
http://arxiv.org/abs/2405.08470
Synthetic Aperture Radar (SAR) images are commonly utilized in military applications for automatic target recognition (ATR). Machine learning (ML) methods, such as Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN), are frequently us
Externí odkaz:
http://arxiv.org/abs/2401.02687
Sparse Matricized Tensor Times Khatri-Rao Product (spMTTKRP) is the most time-consuming compute kernel in sparse tensor decomposition. In this paper, we introduce a novel algorithm to minimize the execution time of spMTTKRP across all modes of an inp
Externí odkaz:
http://arxiv.org/abs/2309.09131
Autor:
Alberto Raggi, Matilde Leonardi, Marco Arruda, Valeria Caponnetto, Matteo Castaldo, Gianluca Coppola, Adriana Della Pietra, Xiangning Fan, David Garcia-Azorin, Parisa Gazerani, Lou Grangeon, Licia Grazzi, Fu-Jung Hsiao, Keiko Ihara, Alejandro Labastida-Ramirez, Kristin Sophie Lange, Marco Lisicki, Alessia Marcassoli, Danilo Antonio Montisano, Dilara Onan, Agnese Onofri, Lanfranco Pellesi, Mario Peres, Igor Petrušić, Bianca Raffaelli, Eloisa Rubio-Beltran, Andreas Straube, Sebastian Straube, Tsubasa Takizawa, Claudio Tana, Michela Tinelli, Massimiliano Valeriani, Simone Vigneri, Doga Vuralli, Marta Waliszewska-Prosół, Wei Wang, Yonggang Wang, William Wells-Gatnik, Tissa Wijeratne, Paolo Martelletti
Publikováno v:
The Journal of Headache and Pain, Vol 25, Iss 1, Pp 1-47 (2024)
Abstract Background and aim Migraine is a common disabling conditions which, globally, affects 15.2% of the population. It is the second cause of health loss in terms of years lived with disability, the first among women. Despite being so common, it
Externí odkaz:
https://doaj.org/article/cbdab7101f184ba19ef009c2d6b3420c
Graph Neural Networks (GNNs) have revolutionized many Machine Learning (ML) applications, such as social network analysis, bioinformatics, etc. GNN inference can be accelerated by exploiting data sparsity in the input graph, vertex features, and inte
Externí odkaz:
http://arxiv.org/abs/2308.02749
Autor:
Gunarathna, Chathuri, Yang, Rebecca, Wijeratne Mudiyanselage, Pabasara, Amarasinghe, Gayashan, Samarasinghalage, Tharushi, Weerasinghe, R.P. Nilmini, Zhao, Hongying, Zhang, Chaoxiang, Liu, Chengyang, Wang, Kaige, Dev Sureshkumar Jayakumari, Sujan
Publikováno v:
Smart and Sustainable Built Environment, 2023, Vol. 13, Issue 4, pp. 828-855.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/SASBE-08-2022-0173
Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is the key technique for remote sensing image recognition. The state-of-the-art works exploit the deep convolutional neural networks (CNNs) for SAR ATR, leading to high computation cos
Externí odkaz:
http://arxiv.org/abs/2305.07119
Autor:
Wijeratne, Yudhanjaya, Marikar, Ishan
Low-rank adaptation (LoRA) and question-answer datasets from large language models have made it much easier for much smaller models to be finetuned to the point where they display sophisticated conversational abilities. In this paper, we present Eluw
Externí odkaz:
http://arxiv.org/abs/2304.12370
Publikováno v:
Environmental Microbiome, Vol 19, Iss 1, Pp 1-28 (2024)
Abstract Background The rhizosphere microbiome displays structural and functional dynamism driven by plant, microbial, and environmental factors. While such plasticity is a well-evidenced determinant of host health, individual and community-level mic
Externí odkaz:
https://doaj.org/article/791e1748ea9d4eb0917444a21d661c99