Zobrazeno 1 - 10
of 9 382
pro vyhledávání: '"Guo,Yuan"'
Autor:
Liu, Yixiu, Zheng, Yuxiang, Xia, Shijie, Guo, Yuan, Li, Jiajun, Tu, Yi, Song, Chaoling, Liu, Pengfei
The deployment of Large Language Models (LLMs) in content generation raises significant safety concerns, particularly regarding the transparency and interpretability of content evaluations. Current methods, primarily focused on binary safety classifi
Externí odkaz:
http://arxiv.org/abs/2407.17075
Autor:
Chern, Steffi, Hu, Zhulin, Yang, Yuqing, Chern, Ethan, Guo, Yuan, Jin, Jiahe, Wang, Binjie, Liu, Pengfei
Previous works on Large Language Models (LLMs) have mainly focused on evaluating their helpfulness or harmlessness. However, honesty, another crucial alignment criterion, has received relatively less attention. Dishonest behaviors in LLMs, such as sp
Externí odkaz:
http://arxiv.org/abs/2406.13261
In this paper, we investigate the performance of large-scale heterogeneous low Earth orbit (LEO) satellite networks in the context of three association schemes. In contrast to existing studies, where single-tier LEO satellite-based network deployment
Externí odkaz:
http://arxiv.org/abs/2405.06978
In this letter, a novel communication paradigm for simultaneous wireless information and power transfer (SWIPT) is proposed, which leverages the thermal characteristics of electromagnetic signals. In particular, the proposed scheme exploits the inher
Externí odkaz:
http://arxiv.org/abs/2403.17792
Autor:
Huang, Guo-yuan
It has been claimed that the coherent scattering of relic neutrinos with the Earth will result in a neutrino-antineutrino asymmetry of $\mathcal{O}(10^{-4})$ on the Earth surface, which is five orders of magnitude larger than the naive model expectat
Externí odkaz:
http://arxiv.org/abs/2401.07347
Self-supervised monocular depth estimation is of significant importance with applications spanning across autonomous driving and robotics. However, the reliance on self-supervision introduces a strong static-scene assumption, thereby posing challenge
Externí odkaz:
http://arxiv.org/abs/2312.13066
Autor:
Zou, Zi-Xin, Yu, Zhipeng, Guo, Yuan-Chen, Li, Yangguang, Liang, Ding, Cao, Yan-Pei, Zhang, Song-Hai
Recent advancements in 3D reconstruction from single images have been driven by the evolution of generative models. Prominent among these are methods based on Score Distillation Sampling (SDS) and the adaptation of diffusion models in the 3D domain.
Externí odkaz:
http://arxiv.org/abs/2312.09147
Diffusion models trained on large-scale text-image datasets have demonstrated a strong capability of controllable high-quality image generation from arbitrary text prompts. However, the generation quality and generalization ability of 3D diffusion mo
Externí odkaz:
http://arxiv.org/abs/2312.09069
Autor:
Yang, Yunhan, Huang, Yukun, Wu, Xiaoyang, Guo, Yuan-Chen, Zhang, Song-Hai, Zhao, Hengshuang, He, Tong, Liu, Xihui
Utilizing pre-trained 2D large-scale generative models, recent works are capable of generating high-quality novel views from a single in-the-wild image. However, due to the lack of information from multiple views, these works encounter difficulties i
Externí odkaz:
http://arxiv.org/abs/2312.03611
Autor:
Yao, Ni, Hu, Hang, Chen, Kaicong, Zhao, Chen, Guo, Yuan, Li, Boya, Nan, Jiaofen, Li, Yanting, Han, Chuang, Zhu, Fubao, Zhou, Weihua, Tian, Li
Objectives To develop and validate a deep learning-based diagnostic model incorporating uncertainty estimation so as to facilitate radiologists in the preoperative differentiation of the pathological subtypes of renal cell carcinoma (RCC) based on CT
Externí odkaz:
http://arxiv.org/abs/2311.00567