Zobrazeno 1 - 10
of 3 173
pro vyhledávání: '"A. Moalla"'
State-of-the-art LLMs often rely on scale with high computational costs, which has sparked a research agenda to reduce parameter counts and costs without significantly impacting performance. Our study focuses on Transformer-based LLMs, specifically a
Externí odkaz:
http://arxiv.org/abs/2407.09835
State-of-the-art results in large language models (LLMs) often rely on scale, which becomes computationally expensive. This has sparked a research agenda to reduce these models' parameter counts and computational costs without significantly impacting
Externí odkaz:
http://arxiv.org/abs/2406.16450
Reinforcement learning (RL) is inherently rife with non-stationarity since the states and rewards the agent observes during training depend on its changing policy. Therefore, networks in deep RL must be capable of adapting to new observations and fit
Externí odkaz:
http://arxiv.org/abs/2405.00662
Autor:
Jarboui, Anis, Moalla, Marwa
Publikováno v:
Journal of Financial Reporting and Accounting, 2022, Vol. 22, Issue 5, pp. 1284-1313.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JFRA-11-2021-0403
Autor:
David Cohen, Kyra Jasper, Alisha Zhao, Khadija Taoufik Moalla, Kasirim Nwuke, Sophia Nesamoney, Gary L. Darmstadt
Publikováno v:
Global Public Health, Vol 19, Iss 1 (2024)
To achieve Sustainable Development Goal 5 for gender equality by 2030, it is crucial for health and development professionals and governmental officials to understand how legal systems empower or oppress populations on the basis of gender worldwide,
Externí odkaz:
https://doaj.org/article/8dcb87daebd547f787c7161f57def65e
Publikováno v:
مجلة جامعة تشرين للبحوث والدراسات العلمية، سلسلة العلوم الأساسية, Vol 46, Iss 3 (2024)
تم في هذه الدراسة تطوير طريقة تحليلية حساسة ودقيقة في هذه الدراسة لتحديد وكشف مركب الكركمين باستخدام الكروماتوغرافيا السائلة عالية ال
Externí odkaz:
https://doaj.org/article/635c83189a924d08b2a0adbdf1ef8471
Autor:
Ellis, Benjamin, Cook, Jonathan, Moalla, Skander, Samvelyan, Mikayel, Sun, Mingfei, Mahajan, Anuj, Foerster, Jakob N., Whiteson, Shimon
The availability of challenging benchmarks has played a key role in the recent progress of machine learning. In cooperative multi-agent reinforcement learning, the StarCraft Multi-Agent Challenge (SMAC) has become a popular testbed for centralised tr
Externí odkaz:
http://arxiv.org/abs/2212.07489
Autor:
Faiçal Farhat, Achraf Ammar, Nourhen Mezghani, Mohamed Moncef Kammoun, Khaled Trabelsi, Haitham Jahrami, Adnene Gharbi, Lassad Sallemi, Haithem Rebai, Wassim Moalla, Bouwien Smits-Engelsman
Publikováno v:
European Journal of Investigation in Health, Psychology and Education, Vol 14, Iss 4, Pp 1028-1043 (2024)
The present study aimed to examine precision and variability in dart throwing performance and the relationships between these outcomes and bouncing, throwing and catching tasks in children with and without DCD. Children between the ages of 8 and 10 y
Externí odkaz:
https://doaj.org/article/b4308976b925416f97114068ce0c3af3
Autor:
Walha, Yasmin, Rekik, Mona, Moalla, Khadija Sonda, Kammoun, Sonda, Ayadi, Omar, Mhiri, Chokri, Dammak, Mariem, Trigui, Amira
Publikováno v:
In eNeurologicalSci December 2024 37
Publikováno v:
BMC Sports Science, Medicine and Rehabilitation, Vol 16, Iss 1, Pp 1-9 (2024)
Abstract Background In sports sciences, normative data serve as standards for specific physical performance attributes, enhancing talent identification within a specific population. The aim of this study was to provide standard data for Agility-15 m,
Externí odkaz:
https://doaj.org/article/7f19f6d485534c2596968f19d4e42c98