Zobrazeno 1 - 10
of 3 120
pro vyhledávání: '"Laudon, A."'
Domain-specific adaptation is critical to maximizing the performance of pre-trained language models (PLMs) on one or multiple targeted tasks, especially under resource-constrained use cases, such as edge devices. However, existing methods often strug
Externí odkaz:
http://arxiv.org/abs/2410.10181
Autor:
Ismailalwali Babikir, Abdul Halim Abdul Latiff, Mohamed Elsaadany, Hadyan Pratama, Muhammad Sajid, Salbiah Mad Sahad, Muhammad Anwar Ishak, Carolan Laudon
Publikováno v:
Geomechanics and Geophysics for Geo-Energy and Geo-Resources, Vol 10, Iss 1, Pp 1-4 (2024)
Externí odkaz:
https://doaj.org/article/470273be0c4240d9a20613445dbdbfa5
Autor:
Zhou, Yanqi, Du, Nan, Huang, Yanping, Peng, Daiyi, Lan, Chang, Huang, Da, Shakeri, Siamak, So, David, Dai, Andrew, Lu, Yifeng, Chen, Zhifeng, Le, Quoc, Cui, Claire, Laudon, James, Dean, Jeff
Transformers are central to recent successes in natural language processing and computer vision. Transformers have a mostly uniform backbone where layers alternate between feed-forward and self-attention in order to build a deep network. Here we inve
Externí odkaz:
http://arxiv.org/abs/2306.00008
Autor:
Hu, Yi, Zhang, Chaoran, Andert, Edward, Singh, Harshul, Shrivastava, Aviral, Laudon, James, Zhou, Yanqi, Iannucci, Bob, Joe-Wong, Carlee
Careful placement of a computational application within a target device cluster is critical for achieving low application completion time. The problem is challenging due to its NP-hardness and combinatorial nature. In recent years, learning-based app
Externí odkaz:
http://arxiv.org/abs/2305.14562
Pretraining on a large-scale corpus has become a standard method to build general language models (LMs). Adapting a model to new data distributions targeting different downstream tasks poses significant challenges. Naive fine-tuning may incur catastr
Externí odkaz:
http://arxiv.org/abs/2305.12281
Autor:
Ismailalwali Babikir, Abdul Halim Abdul Latiff, Mohamed Elsaadany, Hadyan Pratama, Muhammad Sajid, Salbiah Mad Sahad, Muhammad Anwar Ishak, Carolan Laudon
Publikováno v:
Geomechanics and Geophysics for Geo-Energy and Geo-Resources, Vol 10, Iss 1, Pp 1-29 (2024)
Abstract Over the past few years, the use of machine learning has gained considerable momentum in many industries, including exploration seismic. While supervised machine learning is increasingly being used in seismic data analysis, some obstacles hi
Externí odkaz:
https://doaj.org/article/4a2bc18eed3148579e3091edde98cfda
Autor:
Keira Johnson, Kathi Jo Jankowski, Joanna Carey, Nicholas J. Lyon, William H. McDowell, Arial Shogren, Adam Wymore, Lienne Sethna, Wilfred M. Wollheim, Amanda E. Poste, Pirkko Kortelainen, Ruth Heindel, Hjalmar Laudon, Antti Räike, Jeremy B. Jones, Diane McKnight, Paul Julian, Sidney Bush, Pamela L. Sullivan
Publikováno v:
Limnology and Oceanography Letters, Vol 9, Iss 3, Pp 237-246 (2024)
Abstract Fluvial silicon (Si) plays a critical role in controlling primary production, water quality, and carbon sequestration through supporting freshwater and marine diatom communities. Geological, biogeochemical, and hydrological processes, as wel
Externí odkaz:
https://doaj.org/article/ac1f931e7d6441a4b2de5d78536d11a1
Autor:
Johannes Larson, Carl Vigren, Jörgen Wallerman, Anneli M. Ågren, Alex Appiah Mensah, Hjalmar Laudon
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract Forest growth varies across landscapes due to the intricate relationships between various environmental drivers and forest management. In this study, we analysed the variation of tree growth potential across a landscape scale and its relatio
Externí odkaz:
https://doaj.org/article/8cd4bd3422c54a4686f5230089afbbac
Autor:
Palviainen, Marjo, Pumpanen, Jukka, Mosquera, Virginia, Hasselquist, Eliza Maher, Laudon, Hjalmar, Ostonen, Ivika, Kull, Ain, Wilson, Florence Renou, Peltomaa, Elina, Könönen, Mari, Launiainen, Samuli, Peltola, Heli, Ojala, Anne, Laurén, Annamari
Publikováno v:
In Science of the Total Environment 10 November 2024 950
Autor:
Laudon, Aksel D., Beaulieu-Jones, Brendin R., Gitonga, Baraka, Yang, Frank F., Chen, Elizabeth, Flum, Dave R., Lerner, Kasey, Evans, Heather L., Thompson, Lauren, Azar, Faris K., Charboneau, Alex, Simianu, Vlad V., Sanchez, Sabrina E., Drake, F. Thurston
Publikováno v:
In Journal of Surgical Research October 2024 302:428-436