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Akademický článek

Distilled-MobileNet Model of Convolutional Neural Network Simplified Structure for Plant Disease Recognition

Autor: QIU Wenjie, YE Jin, HU Liangqing, YANG Juan, LI Qili, MO Jianyou, YI Wanmao

Publikováno v: 智慧农业, Vol 3, Iss 1, Pp 109-117 (2021)

The development of convolutional neural networks(CNN) has brought a large number of network parameters and huge model volumes, which greatly limites the application on devices with small computing resources, such as single-chip microcomputers and mob

Externí odkaz: https://doaj.org/article/f84d60be63634dc9b10de6e32d98663f

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Q-Adapter: Customizing Pre-trained LLMs to New Preferences with Forgetting Mitigation

Autor: Li, Yi-Chen, Zhang, Fuxiang, Qiu, Wenjie, Yuan, Lei, Jia, Chengxing, Zhang, Zongzhang, Yu, Yang, An, Bo

Large Language Models (LLMs), trained on a large amount of corpus, have demonstrated remarkable abilities. However, it may not be sufficient to directly apply open-source LLMs like Llama to certain real-world scenarios, since most of them are trained

Externí odkaz: http://arxiv.org/abs/2407.03856

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Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics

Autor: Zhang, Xinyu, Qiu, Wenjie, Li, Yi-Chen, Yuan, Lei, Jia, Chengxing, Zhang, Zongzhang, Yu, Yang

Developing policies that can adjust to non-stationary environments is essential for real-world reinforcement learning applications. However, learning such adaptable policies in offline settings, with only a limited set of pre-collected trajectories,

Externí odkaz: http://arxiv.org/abs/2402.11317

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