Zobrazeno 1 - 10
of 1 014
pro vyhledávání: '"CHEN, Yixuan"'
Despite continuous advancements in deep learning for understanding human motion, existing models often struggle to accurately identify action timing and specific body parts, typically supporting only single-round interaction. Such limitations in capt
Externí odkaz:
http://arxiv.org/abs/2410.11404
Autor:
Wang, Chenyu, Yan, Shuo, Chen, Yixuan, Wang, Yujiang, Dong, Mingzhi, Yang, Xiaochen, Li, Dongsheng, Dick, Robert P., Lv, Qin, Yang, Fan, Lu, Tun, Gu, Ning, Shang, Li
Video generation using diffusion-based models is constrained by high computational costs due to the frame-wise iterative diffusion process. This work presents a Diffusion Reuse MOtion (Dr. Mo) network to accelerate latent video generation. Our key di
Externí odkaz:
http://arxiv.org/abs/2409.12532
Autor:
Liu, Junwei, Wang, Kaixin, Chen, Yixuan, Peng, Xin, Chen, Zhenpeng, Zhang, Lingming, Lou, Yiling
The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. Compared to standalone LLMs, LLM-based agents substantially extend the versatility and expertise of LLMs by enhancing LLMs with the cap
Externí odkaz:
http://arxiv.org/abs/2409.02977
In this letter, we study the energy efficiency maximization problem for a fluid antenna system (FAS) in near field communications. Specifically, we consider a point-to-point near-field system where the base station (BS) transmitter has multiple fixed
Externí odkaz:
http://arxiv.org/abs/2407.05791
Repository-level code completion is challenging as it involves complicated contexts from multiple files in the repository. To date, researchers have proposed two technical categories to enhance LLM-based repository-level code completion, i.e., retrie
Externí odkaz:
http://arxiv.org/abs/2406.10018
Autor:
Shi, Yubin, Chen, Yixuan, Dong, Mingzhi, Yang, Xiaochen, Li, Dongsheng, Wang, Yujiang, Dick, Robert P., Lv, Qin, Zhao, Yingying, Yang, Fan, Lu, Tun, Gu, Ning, Shang, Li
Despite their prevalence in deep-learning communities, over-parameterized models convey high demands of computational costs for proper training. This work studies the fine-grained, modular-level learning dynamics of over-parameterized models to attai
Externí odkaz:
http://arxiv.org/abs/2405.07527
Autor:
Xu, Ruopeng, Chen, Yixuan, Kang, Jiawen, Xu, Minrui, Yang, Zhaohui, Huang, Chongwen, Niyato, Dusit
In this paper, we investigate the problem of resource allocation for fluid antenna relay (FAR) system with antenna location optimization. In the considered model, each user transmits information to a base station (BS) with help of FAR. The antenna lo
Externí odkaz:
http://arxiv.org/abs/2404.00628
When deploying fast charging stations (FCSs) to support long-distance trips of electric vehicles (EVs), there exist indirect network effects: while the gradual diffusion of EVs directly influences the timing and capacities of FCS allocation, the deci
Externí odkaz:
http://arxiv.org/abs/2310.07352
Autor:
Du, Xueying, Liu, Mingwei, Wang, Kaixin, Wang, Hanlin, Liu, Junwei, Chen, Yixuan, Feng, Jiayi, Sha, Chaofeng, Peng, Xin, Lou, Yiling
In this work, we make the first attempt to evaluate LLMs in a more challenging code generation scenario, i.e. class-level code generation. We first manually construct the first class-level code generation benchmark ClassEval of 100 class-level Python
Externí odkaz:
http://arxiv.org/abs/2308.01861
Autor:
Chen, Yixuan
Singing Mandarin repertoires is an arduous and intimidating task for most non-nativesingers due to two main challenging aspects. Firstly, the Chinese language is difficult to readand pronounce because the Chinese language is comprised of characters o