Zobrazeno 1 - 10
of 2 273
pro vyhledávání: '"LI, Siyuan"'
This study establishes bounds on the leading-order (LO) hadronic vacuum polarization (HVP) contribution to the anomalous magnetic moment of the muon ($a_\mu^{\mathrm{HVP,LO}}$, $a_\mu = (g-2)_\mu/2$) by using H\"older's inequality and related inequal
Externí odkaz:
http://arxiv.org/abs/2408.15432
Deep Reinforcement Learning (DRL) is regarded as a promising tool for optical network optimization. However, the flexibility and efficiency of current DRL-based solutions for optical network optimization require further improvement. Currently, genera
Externí odkaz:
http://arxiv.org/abs/2406.15906
Unmanned Combat Aerial Vehicle (UCAV) dogfight, which refers to a fight between two or more UCAVs usually at close quarters, plays a decisive role on the aerial battlefields. With the evolution of artificial intelligence, dogfight progressively trans
Externí odkaz:
http://arxiv.org/abs/2406.11562
Autor:
Lin, Haitao, Zhao, Guojiang, Zhang, Odin, Huang, Yufei, Wu, Lirong, Liu, Zicheng, Li, Siyuan, Tan, Cheng, Gao, Zhifeng, Li, Stan Z.
Structure-based drug design (SBDD) aims to generate potential drugs that can bind to a target protein and is greatly expedited by the aid of AI techniques in generative models. However, a lack of systematic understanding persists due to the diverse s
Externí odkaz:
http://arxiv.org/abs/2406.10840
To mitigate the computational complexity in the self-attention mechanism on long sequences, linear attention utilizes computation tricks to achieve linear complexity, while state space models (SSMs) popularize a favorable practice of using non-data-d
Externí odkaz:
http://arxiv.org/abs/2406.08128
Autor:
Tan, Cheng, Lyu, Dongxin, Li, Siyuan, Gao, Zhangyang, Wei, Jingxuan, Ma, Siqi, Liu, Zicheng, Li, Stan Z.
Large Language Models (LLMs) have demonstrated wide-ranging applications across various fields and have shown significant potential in the academic peer-review process. However, existing applications are primarily limited to static review generation
Externí odkaz:
http://arxiv.org/abs/2406.05688
Autor:
Li, Siyuan, Ke, Lei, Danelljan, Martin, Piccinelli, Luigi, Segu, Mattia, Van Gool, Luc, Yu, Fisher
The robust association of the same objects across video frames in complex scenes is crucial for many applications, especially Multiple Object Tracking (MOT). Current methods predominantly rely on labeled domain-specific video datasets, which limits t
Externí odkaz:
http://arxiv.org/abs/2406.04221
Autor:
Liu, Zicheng, Li, Jiahui, Li, Siyuan, Zang, Zelin, Tan, Cheng, Huang, Yufei, Bai, Yajing, Li, Stan Z.
The Genomic Foundation Model (GFM) paradigm is expected to facilitate the extraction of generalizable representations from massive genomic data, thereby enabling their application across a spectrum of downstream applications. Despite advancements, a
Externí odkaz:
http://arxiv.org/abs/2406.01627
Autor:
Tan, Cheng, Wei, Jingxuan, Sun, Linzhuang, Gao, Zhangyang, Li, Siyuan, Yu, Bihui, Guo, Ruifeng, Li, Stan Z.
Large language models equipped with retrieval-augmented generation (RAG) represent a burgeoning field aimed at enhancing answering capabilities by leveraging external knowledge bases. Although the application of RAG with language-only models has been
Externí odkaz:
http://arxiv.org/abs/2405.20834
Autor:
Gan, Chunjing, Yang, Dan, Hu, Binbin, Zhang, Hanxiao, Li, Siyuan, Liu, Ziqi, Shen, Yue, Ju, Lin, Zhang, Zhiqiang, Gu, Jinjie, Liang, Lei, Zhou, Jun
In recent years, large language models (LLMs) have made remarkable achievements in various domains. However, the untimeliness and cost of knowledge updates coupled with hallucination issues of LLMs have curtailed their applications in knowledge inten
Externí odkaz:
http://arxiv.org/abs/2405.19893