Zobrazeno 1 - 10
of 84 367
pro vyhledávání: '"A Moens"'
Autor:
KALLERGIS, KATHERINE, LARSEN, KEITH
Publikováno v:
Real Deal: New York Real Estate. Nov2023, Vol. 21 Issue 11, p50-50. 1p.
Through end-to-end training to predict the next token, LLMs have become valuable tools for various tasks. Enhancing their core training in language modeling can improve numerous downstream applications. A successful approach to enhance language model
Externí odkaz:
http://arxiv.org/abs/2410.12492
Autor:
Kallergis, Katherine
Publikováno v:
Real Deal: New York Real Estate. Aug2022, Vol. 20 Issue 8, p85-85. 2/3p.
Autor:
Mamiya, Yasuharu1 (AUTHOR), Akiba, Mitsuteru2 (AUTHOR), Ekino, Taisuke3 (AUTHOR), Kanzaki, Natsumi4 (AUTHOR)
Publikováno v:
Nematology. 2021, Vol. 23 Issue 8, p909-928. 20p.
Publikováno v:
Jurnal Ilmu Pertanian Indonesia, Vol 25, Iss 4, Pp 540-546 (2020)
Cinchona ledgeriana Moens is an industrial plant producing secondary metabolite quinoline alkaloids. To maintain and moreover, to increase the quinoline production especially quinine, in vitro culture system through cell culture could be a potential
Externí odkaz:
https://doaj.org/article/aa3af1f5dd824b4299b1a1ed799e1765
Modern language models predict the next token in the sequence by considering the past text through a powerful function such as attention. However, language models have no explicit mechanism that allows them to spend computation time for planning long
Externí odkaz:
http://arxiv.org/abs/2409.00070
To assist human fact-checkers, researchers have developed automated approaches for visual misinformation detection. These methods assign veracity scores by identifying inconsistencies between the image and its caption, or by detecting forgeries in th
Externí odkaz:
http://arxiv.org/abs/2408.09939
Parameter-efficient fine-tuning (PEFT) methods are increasingly used with pre-trained language models (PLMs) for continual learning (CL). These methods typically involve training a PEFT module for each new task and employing similarity-based selectio
Externí odkaz:
http://arxiv.org/abs/2408.09053
Link prediction models can benefit from incorporating textual descriptions of entities and relations, enabling fully inductive learning and flexibility in dynamic graphs. We address the challenge of also capturing rich structured information about th
Externí odkaz:
http://arxiv.org/abs/2408.06778
The ever-growing volume of biomedical publications creates a critical need for efficient knowledge discovery. In this context, we introduce an open-source end-to-end framework designed to construct knowledge around specific diseases directly from raw
Externí odkaz:
http://arxiv.org/abs/2407.13492