Zobrazeno 1 - 10
of 1 646
pro vyhledávání: '"Curriculum Learning"'
Autor:
Nosheen Abid, Md Kislu Noman, György Kovács, Syed Mohammed Shamsul Islam, Tosin Adewumi, Paul Lavery, Faisal Shafait, Marcus Liwicki
Publikováno v:
Ecological Informatics, Vol 83, Iss , Pp 102804- (2024)
Seagrass ecosystems are pivotal in marine environments, serving as crucial habitats for diverse marine species and contributing significantly to carbon sequestration. Accurate classification of seagrass species from underwater images is imperative fo
Externí odkaz:
https://doaj.org/article/b093086ca337487093dc10cca0d7b16e
Autor:
Riccardo Berta, Luca Lazzaroni, Alessio Capello, Marianna Cossu, Luca Forneris, Alessandro Pighetti, Francesco Bellotti
Publikováno v:
Journal of Intelligent and Connected Vehicles, Vol 7, Iss 3, Pp 229-244 (2024)
This study provides a systematic analysis of the resource-consuming training of deep reinforcement-learning (DRL) agents for simulated low-speed automated driving (AD). In Unity, this study established two case studies: garage parking and navigating
Externí odkaz:
https://doaj.org/article/256d948e213b4d94be3c15fae9c7f11e
Autor:
Ruonan WANG, Qi DONG
Publikováno v:
工程科学学报, Vol 46, Iss 7, Pp 1251-1268 (2024)
Reinforcement learning, a cornerstone in the expansive landscape of artificial intelligence, has asserted its dominance as the prevailing methodology in contemporary multiagent system decision-making because of its formidable efficacy. However, the p
Externí odkaz:
https://doaj.org/article/d701715089f8410fb8a870220448a52a
Publikováno v:
Edusaintek, Vol 12, Iss 1 (2024)
The independent curriculum was developed as a more flexible curriculum framework, centered on basic material and developing students' uniqueness and abilities. One way that can be used to develop students' abilities is through learning media that is
Externí odkaz:
https://doaj.org/article/fb0e3d3d6bd94785b5d7a48ac7b2274e
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 5, Pp 6213-6229 (2024)
Abstract Bayesian networks (BNs) are highly effective in handling uncertain problems, which can assist in decision-making by reasoning with limited and incomplete information. Learning a faithful directed acyclic graph (DAG) from a large number of co
Externí odkaz:
https://doaj.org/article/a45c4d1a640e463285f4020b0ac65222
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)
Abstract This work addresses a critical issue: the deterioration of concrete structures due to fine-grained cracks, which compromises their strength and longevity. To tackle this problem, experts have turned to computer vision (CV) based automated st
Externí odkaz:
https://doaj.org/article/6aed467d02c441e3b6389b7eca784c45
Publikováno v:
Jisuanji kexue yu tansuo, Vol 18, Iss 6, Pp 1590-1599 (2024)
On the one hand, the existing knowledge graph entity prediction methods only use the neighborhood and graph structure information to enhance the node information, and ignore the multi-modal information outside the knowledge graph to enhance the knowl
Externí odkaz:
https://doaj.org/article/cef40bb53cc84431bcb3c90a64bcd03f
Autor:
WANG Haiyong, PAN Haitao
Publikováno v:
Jisuanji kexue yu tansuo, Vol 18, Iss 6, Pp 1627-1636 (2024)
In recent years, the sample mining strategy has been integrated into the loss function of face recognition, significantly improving the performance of face recognition. But most of the work focuses on how to mine difficult samples during the training
Externí odkaz:
https://doaj.org/article/1b5c6c1b73dd46bda61cb720cf498345
Autor:
Sung-Jae Lee, Hyun Jun Oh, Young-Don Son, Jong-Hoon Kim, Ik-Jae Kwon, Bongju Kim, Jong-Ho Lee, Hang-Keun Kim
Publikováno v:
BMC Oral Health, Vol 24, Iss 1, Pp 1-10 (2024)
Abstract Background Oral potentially malignant disorders (OPMDs) are associated with an increased risk of cancer of the oral cavity including the tongue. The early detection of oral cavity cancers and OPMDs is critical for reducing cancer-specific mo
Externí odkaz:
https://doaj.org/article/0bdbd723717346909ca5061048aa7100
Autor:
Shonal Chaudhry, Anuraganand Sharma
Publikováno v:
IEEE Access, Vol 12, Pp 138429-138440 (2024)
The order of training samples can have a significant impact on a model’s performance. Curriculum learning is an approach for gradually training a model by ordering samples from ‘easy’ to ‘hard’. This paper proposes the novel idea of a curri
Externí odkaz:
https://doaj.org/article/c7c9f470b0d2473283ae9424a7c0a024