Zobrazeno 1 - 10
of 1 448
pro vyhledávání: '"CHEN Minghui"'
Publikováno v:
Jixie chuandong, Vol 47, Pp 145-150 (2023)
In order to stably exert the best obstacle-surmounting performance of the wheel-track compound robot, a new wheel-track compound robot that can switch multiple motion modes according to the environment is developed. The robot can switch between the u
Externí odkaz:
https://doaj.org/article/68b7ee4a6c914f3c9abbbf5ca073d5dd
Publikováno v:
Fushe yanjiu yu fushe gongyi xuebao, Vol 42, Iss 1, Pp 010301-010301 (2024)
To further understand the role of low-energy N+ implantation in the phylogenetic evolution and characterization of drug resistance in Escherichia coli (E.coli), this study used low-energy N+ ion implantation to screen for drug resistant E.coli. The 1
Externí odkaz:
https://doaj.org/article/59195a49618244a29c570d9ebfedae27
Autor:
Deng, Wenlong, Zhao, Yize, Vakilian, Vala, Chen, Minghui, Li, Xiaoxiao, Thrampoulidis, Christos
Storing open-source fine-tuned models separately introduces redundancy and increases response times in applications utilizing multiple models. Delta-parameter pruning (DPP), particularly the random drop and rescale (DARE) method proposed by Yu et al.
Externí odkaz:
http://arxiv.org/abs/2410.09344
In this paper, we introduce a nonparametric end-to-end method for probabilistic forecasting of distributed renewable generation outputs while including missing data imputation. Firstly, we employ a nonparametric probabilistic forecast model utilizing
Externí odkaz:
http://arxiv.org/abs/2404.00729
In the era of Foundation Models' (FMs) rising prominence in AI, our study addresses the challenge of biases in medical images while the model operates in black-box (e.g., using FM API), particularly spurious correlations between pixels and sensitive
Externí odkaz:
http://arxiv.org/abs/2403.06104
Cross-silo federated learning (FL) enables the development of machine learning models on datasets distributed across data centers such as hospitals and clinical research laboratories. However, recent research has found that current FL algorithms face
Externí odkaz:
http://arxiv.org/abs/2307.10507
The advent of Federated Learning (FL) has revolutionized the way distributed systems handle collaborative model training while preserving user privacy. Recently, Federated Unlearning (FU) has emerged to address demands for the "right to be forgotten"
Externí odkaz:
http://arxiv.org/abs/2306.02216
Invariance to diverse types of image corruption, such as noise, blurring, or colour shifts, is essential to establish robust models in computer vision. Data augmentation has been the major approach in improving the robustness against common corruptio
Externí odkaz:
http://arxiv.org/abs/2204.11531
Publikováno v:
In Journal of Building Engineering 15 November 2024 97