Zobrazeno 1 - 10
of 13 998
pro vyhledávání: '"Chen Zhang"'
Autor:
Shen, Zhuowen, Liu, Yuan, Chen, Zhang, Li, Zhong, Wang, Jiepeng, Liang, Yongqing, Yu, Zhengming, Zhang, Jingdong, Xu, Yi, Schaefer, Scott, Li, Xin, Wang, Wenping
Gaussian splatting has achieved impressive improvements for both novel-view synthesis and surface reconstruction from multi-view images. However, current methods still struggle to reconstruct high-quality surfaces from only sparse view input images u
Externí odkaz:
http://arxiv.org/abs/2412.15400
Abstraction is an important and useful concept in the field of artificial intelligence. To the best of our knowledge, there is no syntactic method to compute a sound and complete abstraction from a given low-level basic action theory and a refinement
Externí odkaz:
http://arxiv.org/abs/2412.11217
Recently, several studies have combined Gaussian Splatting to obtain scene representations with language embeddings for open-vocabulary 3D scene understanding. While these methods perform well, they essentially require very dense multi-view inputs, l
Externí odkaz:
http://arxiv.org/abs/2412.02245
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