Zobrazeno 1 - 10
of 140 924
pro vyhledávání: '"level of training"'
Autor:
Philip, Haddad, Tashu, Tsegaye Misikir
Automatic Essay Scoring (AES) is widely used to evaluate candidates for educational purposes. However, due to the lack of representative data, most existing AES systems are not robust, and their scoring predictions are biased towards the most represe
Externí odkaz:
http://arxiv.org/abs/2409.04795
Autor:
Diana L. Howles
Surpass the Basics of Virtual TrainingNext Level Virtual Training, by Diana L. Howles, has received awards from Axiom Business Book Awards, Goody Business Book Awards, and North American Book Awards.As virtual training continues as a go-to, effective
3D LiDAR-based place recognition remains largely underexplored in horticultural environments, which present unique challenges due to their semi-permeable nature to laser beams. This characteristic often results in highly similar LiDAR scans from adja
Externí odkaz:
http://arxiv.org/abs/2405.19038
Autor:
Kim, Junhan, Lee, Chungman, Cho, Eulrang, Park, Kyungphil, Kim, Ho-young, Kim, Joonyoung, Jeon, Yongkweon
With the increasing complexity of generative AI models, post-training quantization (PTQ) has emerged as a promising solution for deploying hyper-scale models on edge devices such as mobile and TVs. Existing PTQ schemes, however, consume considerable
Externí odkaz:
http://arxiv.org/abs/2402.08958
Akademický článek
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Autor:
Hazel J. Jenkins, Benjamin T. Brown, Mary O’Keeffe, Niamh Moloney, Chris G. Maher, Mark Hancock
Publikováno v:
BMC Medical Education, Vol 24, Iss 1, Pp 1-9 (2024)
Abstract Background The management of low back pain (LBP) is highly variable and patients often receive management that is not recommended and/or miss out on recommended care. Clinician knowledge and behaviours are strongly influenced by entry-level
Externí odkaz:
https://doaj.org/article/06698498a2d84230b74f71dac684f534
Efficient time series forecasting has become critical for real-world applications, particularly with deep neural networks (DNNs). Efficiency in DNNs can be achieved through sparse connectivity and reducing the model size. However, finding the sparsit
Externí odkaz:
http://arxiv.org/abs/2305.18382
Akademický článek
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Akademický článek
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Autor:
Fan, Shuai, Lin, Chen, Li, Haonan, Lin, Zhenghao, Su, Jinsong, Zhang, Hang, Gong, Yeyun, Guo, Jian, Duan, Nan
Most existing pre-trained language representation models (PLMs) are sub-optimal in sentiment analysis tasks, as they capture the sentiment information from word-level while under-considering sentence-level information. In this paper, we propose Senti
Externí odkaz:
http://arxiv.org/abs/2210.09803