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pro vyhledávání: '"Chen Zhang"'
Stationary balance control is challenging for single-track two-wheeled (STTW) robots due to the lack of elegant balancing mechanisms and the conflict between the limited attraction domain and external disturbances. To address the absence of balancing
Externí odkaz:
http://arxiv.org/abs/2410.19615
Immersive scene generation, notably panorama creation, benefits significantly from the adaptation of large pre-trained text-to-image (T2I) models for multi-view image generation. Due to the high cost of acquiring multi-view images, tuning-free genera
Externí odkaz:
http://arxiv.org/abs/2408.02157
Large Language Models (LLMs) have demonstrated significant potential in causal discovery tasks by utilizing their vast expert knowledge from extensive text corpora. However, the multi-agent capabilities of LLMs in causal discovery remain underexplore
Externí odkaz:
http://arxiv.org/abs/2407.15073
Autor:
Qi, Biqing, Zhang, Kaiyan, Tian, Kai, Li, Haoxiang, Chen, Zhang-Ren, Zeng, Sihang, Hua, Ermo, Jinfang, Hu, Zhou, Bowen
The rapid growth of biomedical knowledge has outpaced our ability to efficiently extract insights and generate novel hypotheses. Large language models (LLMs) have emerged as a promising tool to revolutionize knowledge interaction and potentially acce
Externí odkaz:
http://arxiv.org/abs/2407.08940
Autor:
Haijie, Xu, Chen, Zhang
This paper proposes a multivariate nonlinear function-on-function regression model, which allows both the response and the covariates can be multi-dimensional functions. The model is built upon the multivariate functional reproducing kernel Hilbert s
Externí odkaz:
http://arxiv.org/abs/2406.19021
Autor:
Chen, Zhang, Wang, Bixiang
This paper is mainly concerned with the large deviation principle of the fractional McKean-Vlasov stochastic reaction-diffusion equation defined on R^n with polynomial drift of any degree. We first prove the well-posedness of the underlying equation
Externí odkaz:
http://arxiv.org/abs/2406.10694
Autor:
Chen, Zhang, Demetrio, Luca, Gupta, Srishti, Feng, Xiaoyi, Xia, Zhaoqiang, Cinà, Antonio Emanuele, Pintor, Maura, Oneto, Luca, Demontis, Ambra, Biggio, Battista, Roli, Fabio
Thanks to their extensive capacity, over-parameterized neural networks exhibit superior predictive capabilities and generalization. However, having a large parameter space is considered one of the main suspects of the neural networks' vulnerability t
Externí odkaz:
http://arxiv.org/abs/2406.10090
Autor:
Huang, Yuzhong, Li, Zhong, Chen, Zhang, Ren, Zhiyuan, Lin, Guosheng, Morstatter, Fred, Xu, Yi
In the evolving landscape of text-to-3D technology, Dreamfusion has showcased its proficiency by utilizing Score Distillation Sampling (SDS) to optimize implicit representations such as NeRF. This process is achieved through the distillation of pretr
Externí odkaz:
http://arxiv.org/abs/2406.10000
Autor:
Zhang, Kaiyan, Zeng, Sihang, Hua, Ermo, Ding, Ning, Chen, Zhang-Ren, Ma, Zhiyuan, Li, Haoxin, Cui, Ganqu, Qi, Biqing, Zhu, Xuekai, Lv, Xingtai, Jinfang, Hu, Liu, Zhiyuan, Zhou, Bowen
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains and are moving towards more specialized areas. Recent advanced proprietary models such as GPT-4 and Gemini have achieved significant advancements in biomedi
Externí odkaz:
http://arxiv.org/abs/2406.03949
Autor:
Sidahmed, Hakim, Phatale, Samrat, Hutcheson, Alex, Lin, Zhuonan, Chen, Zhang, Yu, Zac, Jin, Jarvis, Chaudhary, Simral, Komarytsia, Roman, Ahlheim, Christiane, Zhu, Yonghao, Li, Bowen, Ganesh, Saravanan, Byrne, Bill, Hoffmann, Jessica, Mansoor, Hassan, Li, Wei, Rastogi, Abhinav, Dixon, Lucas
While Reinforcement Learning from Human Feedback (RLHF) effectively aligns pretrained Large Language and Vision-Language Models (LLMs, and VLMs) with human preferences, its computational cost and complexity hamper its wider adoption. To alleviate som
Externí odkaz:
http://arxiv.org/abs/2403.10704