Zobrazeno 1 - 10
of 1 463
pro vyhledávání: '"Kay Chen"'
Publikováno v:
BMJ Open Gastroenterology, Vol 10, Iss 1 (2023)
Objective Although appendiceal cancer remains a rare gastrointestinal malignancy compared with colorectal cancer, incidence rates of appendiceal cancer have increased in the last two decades. Appendiceal and cecal adenocarcinomas have distinct genomi
Externí odkaz:
https://doaj.org/article/c4be0b0da2b64bbf9bfc64e715bc5c3d
Cross-project defect prediction (CPDP) leverages machine learning (ML) techniques to proactively identify software defects, especially where project-specific data is scarce. However, developing a robust ML pipeline with optimal hyperparameters that e
Externí odkaz:
http://arxiv.org/abs/2411.06491
In this survey, we introduce Meta-Black-Box-Optimization~(MetaBBO) as an emerging avenue within the Evolutionary Computation~(EC) community, which incorporates Meta-learning approaches to assist automated algorithm design. Despite the success of Meta
Externí odkaz:
http://arxiv.org/abs/2411.00625
Speech enhancement is critical for improving speech intelligibility and quality in various audio devices. In recent years, deep learning-based methods have significantly improved speech enhancement performance, but they often come with a high computa
Externí odkaz:
http://arxiv.org/abs/2410.04785
Publikováno v:
Complex & Intelligent Systems, Vol 9, Iss 2, Pp 1115-1116 (2023)
Externí odkaz:
https://doaj.org/article/2a314fe4f5204731b0ad5f47a0997707
Publikováno v:
Case Reports in Oncology, Vol 14, Iss 1, Pp 56-61 (2021)
Hepatocellular carcinoma commonly metastasizes to organs, but there are few reports of vertebral metastases causing cord compression. Here, we present a case of thoracic cord compression in a patient with advanced hepatocellular carcinoma. Providers
Externí odkaz:
https://doaj.org/article/f1c2bb54b42b4e4e98296ab9f74938ea
Model merging is a technique that combines multiple large pretrained models into a single model with enhanced performance and broader task adaptability. It has gained popularity in large pretrained model development due to its ability to bypass the n
Externí odkaz:
http://arxiv.org/abs/2409.18893
Evolutionary Multi-task Optimization (EMTO) is a paradigm that leverages knowledge transfer across simultaneously optimized tasks for enhanced search performance. To facilitate EMTO's performance, various knowledge transfer models have been developed
Externí odkaz:
http://arxiv.org/abs/2409.04270
Spiking Neural Networks (SNNs) hold great potential to realize brain-inspired, energy-efficient computational systems. However, current SNNs still fall short in terms of multi-scale temporal processing compared to their biological counterparts. This
Externí odkaz:
http://arxiv.org/abs/2408.14917
Transferable neural architecture search (TNAS) has been introduced to design efficient neural architectures for multiple tasks, to enhance the practical applicability of NAS in real-world scenarios. In TNAS, architectural knowledge accumulated in pre
Externí odkaz:
http://arxiv.org/abs/2408.11330