Zobrazeno 1 - 10
of 6 832
pro vyhledávání: '"Yanzhao An"'
Publikováno v:
Case Studies in Thermal Engineering, Vol 61, Iss , Pp 105085- (2024)
The air inlet holes on the swirl combustor are crucial for high-performance micro gas turbine (MGT), affecting internal airflow, fuel-air mixing, temperature distribution, and cooling. However, there are seldom studies that pay attention to the influ
Externí odkaz:
https://doaj.org/article/606ab5c9be564c5895882d99a23ed3ab
Publikováno v:
Green Energy and Resources, Vol 1, Iss 1, Pp 100003- (2023)
Regional energy systems are designed to contribute to a green and “carbon neutral” economy of localities. In this system, the engine combustion is significant for power generation. Therefore, this study mainly investigated the effect of throttle
Externí odkaz:
https://doaj.org/article/61ac5d8cae7e4e159ffd181a32b98988
We propose a novel framework, Stable Diffusion-based Momentum Integrated Adversarial Examples (SD-MIAE), for generating adversarial examples that can effectively mislead neural network classifiers while maintaining visual imperceptibility and preserv
Externí odkaz:
http://arxiv.org/abs/2410.13122
Autor:
Jin, Hongpeng, Wu, Yanzhao
The Learning Rate (LR) has a high impact on deep learning training performance. A common practice is to train a Deep Neural Network (DNN) multiple times with different LR policies to find the optimal LR policy, which has been widely recognized as a d
Externí odkaz:
http://arxiv.org/abs/2410.07564
Autor:
Yin, Qingyu, He, Xuzheng, Deng, Luoao, Leong, Chak Tou, Wang, Fan, Yan, Yanzhao, Shen, Xiaoyu, Zhang, Qiang
Fine-tuning and in-context learning (ICL) are two prevalent methods in imbuing large language models with task-specific knowledge. It is commonly believed that fine-tuning can surpass ICL given sufficient training samples as it allows the model to ad
Externí odkaz:
http://arxiv.org/abs/2410.04691
Brain CT report generation is significant to aid physicians in diagnosing cranial diseases. Recent studies concentrate on handling the consistency between visual and textual pathological features to improve the coherence of report. However, there exi
Externí odkaz:
http://arxiv.org/abs/2409.19676
Federated learning is emerging as a promising machine learning technique in the medical field for analyzing medical images, as it is considered an effective method to safeguard sensitive patient data and comply with privacy regulations. However, rece
Externí odkaz:
http://arxiv.org/abs/2409.18907
Transformer-based Mixture-of-Experts (MoE) models have been driving several recent technological advancements in Natural Language Processing (NLP). These MoE models adopt a router mechanism to determine which experts to activate for routing input tok
Externí odkaz:
http://arxiv.org/abs/2409.06669
Autor:
Xie, Qianqian, Li, Dong, Xiao, Mengxi, Jiang, Zihao, Xiang, Ruoyu, Zhang, Xiao, Chen, Zhengyu, He, Yueru, Han, Weiguang, Yang, Yuzhe, Chen, Shunian, Zhang, Yifei, Shen, Lihang, Kim, Daniel, Liu, Zhiwei, Luo, Zheheng, Yu, Yangyang, Cao, Yupeng, Deng, Zhiyang, Yao, Zhiyuan, Li, Haohang, Feng, Duanyu, Dai, Yongfu, Somasundaram, VijayaSai, Lu, Peng, Zhao, Yilun, Long, Yitao, Xiong, Guojun, Smith, Kaleb, Yu, Honghai, Lai, Yanzhao, Peng, Min, Nie, Jianyun, Suchow, Jordan W., Liu, Xiao-Yang, Wang, Benyou, Lopez-Lira, Alejandro, Huang, Jimin, Ananiadou, Sophia
Large language models (LLMs) have advanced financial applications, yet they often lack sufficient financial knowledge and struggle with tasks involving multi-modal inputs like tables and time series data. To address these limitations, we introduce \t
Externí odkaz:
http://arxiv.org/abs/2408.11878
Autor:
Qin, Yanzhao, Zhang, Tao, Shen, Yanjun, Luo, Wenjing, Sun, Haoze, Zhang, Yan, Qiao, Yujing, Chen, Weipeng, Zhou, Zenan, Zhang, Wentao, Cui, Bin
Large Language Models (LLMs) have become instrumental across various applications, with the customization of these models to specific scenarios becoming increasingly critical. System message, a fundamental component of LLMs, is consist of carefully c
Externí odkaz:
http://arxiv.org/abs/2408.10943