Zobrazeno 1 - 10
of 5 659
pro vyhledávání: '"Fu Jie"'
With the rapid development of VR technology, the demand for high-quality 3D models is increasing. Traditional methods struggle with efficiency and quality in large-scale customization. This paper introduces a deep-learning framework that generates hi
Externí odkaz:
http://arxiv.org/abs/2409.02376
This paper aims to establish the global well-posedness of the free boundary problem for the incompressible viscous resistive magnetohydrodynamic (MHD) equations. Under the framework of Lagrangian coordinates, a unique global solution exists in the ha
Externí odkaz:
http://arxiv.org/abs/2408.15279
The swift advancement of single-cell RNA sequencing (scRNA-seq) technologies enables the investigation of cellular-level tissue heterogeneity. Cell annotation significantly contributes to the extensive downstream analysis of scRNA-seq data. However,
Externí odkaz:
http://arxiv.org/abs/2408.10511
The scaling of large language models (LLMs) has revolutionized their capabilities in various tasks, yet this growth must be matched with efficient computational strategies. The Mixture-of-Experts (MoE) architecture stands out for its ability to scale
Externí odkaz:
http://arxiv.org/abs/2408.06793
Games with incomplete preferences are an important model for studying rational decision-making in scenarios where players face incomplete information about their preferences and must contend with incomparable outcomes. We study the problem of computi
Externí odkaz:
http://arxiv.org/abs/2408.02860
Deception plays a crucial role in strategic interactions with incomplete information. Motivated by security applications, we study a class of two-player turn-based deterministic games with one-sided incomplete information, in which player 1 (P1) aims
Externí odkaz:
http://arxiv.org/abs/2407.14436
Mixture-of-experts (MoE) is gaining increasing attention due to its unique properties and remarkable performance, especially for language tasks. By sparsely activating a subset of parameters for each token, MoE architecture could increase the model s
Externí odkaz:
http://arxiv.org/abs/2406.18219
Autor:
Du, Wenyu, Cheng, Shuang, Luo, Tongxu, Qiu, Zihan, Huang, Zeyu, Cheung, Ka Chun, Cheng, Reynold, Fu, Jie
Language models (LMs) exhibit impressive performance and generalization capabilities. However, LMs struggle with the persistent challenge of catastrophic forgetting, which undermines their long-term sustainability in continual learning (CL). Existing
Externí odkaz:
http://arxiv.org/abs/2406.17245
Autor:
Du, Jiangshu, Wang, Yibo, Zhao, Wenting, Deng, Zhongfen, Liu, Shuaiqi, Lou, Renze, Zou, Henry Peng, Venkit, Pranav Narayanan, Zhang, Nan, Srinath, Mukund, Zhang, Haoran Ranran, Gupta, Vipul, Li, Yinghui, Li, Tao, Wang, Fei, Liu, Qin, Liu, Tianlin, Gao, Pengzhi, Xia, Congying, Xing, Chen, Cheng, Jiayang, Wang, Zhaowei, Su, Ying, Shah, Raj Sanjay, Guo, Ruohao, Gu, Jing, Li, Haoran, Wei, Kangda, Wang, Zihao, Cheng, Lu, Ranathunga, Surangika, Fang, Meng, Fu, Jie, Liu, Fei, Huang, Ruihong, Blanco, Eduardo, Cao, Yixin, Zhang, Rui, Yu, Philip S., Yin, Wenpeng
This work is motivated by two key trends. On one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and question answering, significantly reducing the time required for many rout
Externí odkaz:
http://arxiv.org/abs/2406.16253
Continual pre-training has increasingly become the predominant approach for adapting Large Language Models (LLMs) to new domains. This process involves updating the pre-trained LLM with a corpus from a new domain, resulting in a shift in the training
Externí odkaz:
http://arxiv.org/abs/2406.14833