Zobrazeno 1 - 10
of 2 213
pro vyhledávání: '"Liu, ChenXi"'
Recent advances in diffusion models and parameter-efficient fine-tuning (PEFT) have made text-to-image generation and customization widely accessible, with Low Rank Adaptation (LoRA) able to replicate an artist's style or subject using minimal data a
Externí odkaz:
http://arxiv.org/abs/2412.12048
Statistical watermarking techniques are well-established for sequentially decoded language models (LMs). However, these techniques cannot be directly applied to order-agnostic LMs, as the tokens in order-agnostic LMs are not generated sequentially. I
Externí odkaz:
http://arxiv.org/abs/2410.13805
Autor:
Xi, Ningyuan, Wu, Yetao, Fan, Kun, Chen, Teng, Gu, Qingqing, Yu, Peng, Qu, Jinxian, Liu, Chenxi, Jiang, Zhonglin, Chen, Yong, Ji, Luo
Large Language Models (LLM) often needs to be Continual Pre-Trained (CPT) to obtain the unfamiliar language skill or adapt into new domains. The huge training cost of CPT often asks for cautious choice of key hyper-parameters such as the mixture rati
Externí odkaz:
http://arxiv.org/abs/2409.06624
Autor:
Wu, Yetao, Wang, Yihong, Chen, Teng, Liu, Chenxi, Xi, Ningyuan, Gu, Qingqing, Lei, Hongyang, Jiang, Zhonglin, Chen, Yong, Ji, Luo
Hallucinations is a major challenge for large language models (LLMs), prevents adoption in diverse fields. Uncertainty estimation could be used for alleviating the damages of hallucinations. The skeptical emotion of human could be useful for enhancin
Externí odkaz:
http://arxiv.org/abs/2409.06601
Effective representation of 2D images is fundamental in digital image processing, where traditional methods like raster and vector graphics struggle with sharpness and textural complexity respectively. Current neural fields offer high-fidelity and re
Externí odkaz:
http://arxiv.org/abs/2408.00771
Big mobility datasets (BMD) have shown many advantages in studying human mobility and evaluating the performance of transportation systems. However, the quality of BMD remains poorly understood. This study evaluates biases in BMD and develops mitigat
Externí odkaz:
http://arxiv.org/abs/2407.14541
The spectrum shift from the sub-6G band to the high-frequency band has posed an ever-increasing demand on the paradigm shift from narrowband beamforming to wideband beamforming. Despite recent research efforts, the problem of wideband beamforming des
Externí odkaz:
http://arxiv.org/abs/2407.05293
Integrated sensing and communication (ISAC) has been recognized as a key enabler and feature of future wireless networks. In the existing works analyzing the performances of ISAC, discrete-time systems were commonly assumed, which, however, overlooke
Externí odkaz:
http://arxiv.org/abs/2407.03926
Intelligent reflecting surface (IRS) has the potential to enhance sensing performance, due to its capability of reshaping the echo signals. Different from the existing literature, which has commonly focused on IRS beamforming optimization, in this pa
Externí odkaz:
http://arxiv.org/abs/2407.03902
Autor:
Chen, Lichang, Chen, Jiuhai, Liu, Chenxi, Kirchenbauer, John, Soselia, Davit, Zhu, Chen, Goldstein, Tom, Zhou, Tianyi, Huang, Heng
Reinforcement learning with human feedback~(RLHF) is critical for aligning Large Language Models (LLMs) with human preference. Compared to the widely studied offline version of RLHF, \emph{e.g.} direct preference optimization (DPO), recent works have
Externí odkaz:
http://arxiv.org/abs/2406.07657