Zobrazeno 1 - 10
of 17 977
pro vyhledávání: '"Chen, Lu‐An"'
Speculative Decoding (SD) is a popular lossless technique for accelerating the inference of Large Language Models (LLMs). We show that the decoding speed of SD frameworks with static draft structures can be significantly improved by incorporating con
Externí odkaz:
http://arxiv.org/abs/2412.18910
In this paper, we consider the following Hardy-type mean field equation \[ \left\{ {\begin{array}{*{20}{c}} { - \Delta u-\frac{1}{(1-|x|^2)^2} u = \lambda e^u}, & {\rm in} \ \ B_1,\\ {\ \ \ \ u = 0,} &\ {\rm on}\ \partial B_1, \end{array}} \right. \]
Externí odkaz:
http://arxiv.org/abs/2412.17886
In this paper, we first establish the quantitative properties for positive solutions to the Moser-Trudinger equations in the two-dimensional Poincar\'e disk $\mathbb{B}^2$: \begin{equation*}\label{mt1} \left\{ \begin{aligned} &-\Delta_{\mathbb{B}^2}u
Externí odkaz:
http://arxiv.org/abs/2412.16890
Conjugate heat transfer is a challenging fluid-structure coupling problem that can significantly influence flame stabilization and thermoacoustic instabilities. To properly capture combustion phenomena that involve conjugate heat transfer, careful mo
Externí odkaz:
http://arxiv.org/abs/2412.13539
The rise of "bedroom producers" has democratized music creation, while challenging producers to objectively evaluate their work. To address this, we present AI TrackMate, an LLM-based music chatbot designed to provide constructive feedback on music p
Externí odkaz:
http://arxiv.org/abs/2412.06617
Autor:
Xu, Hongshen, Zhu, Su, Wang, Zihan, Zheng, Hang, Ma, Da, Cao, Ruisheng, Fan, Shuai, Chen, Lu, Yu, Kai
Large Language Models (LLMs) have extended their capabilities beyond language generation to interact with external systems through tool calling, offering powerful potential for real-world applications. However, the phenomenon of tool hallucinations,
Externí odkaz:
http://arxiv.org/abs/2412.04141
Autor:
Ma, Da, Chen, Lu, Zhang, Situo, Miao, Yuxun, Zhu, Su, Chen, Zhi, Xu, Hongshen, Li, Hanqi, Fan, Shuai, Pan, Lei, Yu, Kai
The increasing context window size in Large Language Models (LLMs), such as the GPT and LLaMA series, has improved their ability to tackle complex, long-text tasks, but at the cost of inference efficiency, particularly regarding memory and computatio
Externí odkaz:
http://arxiv.org/abs/2412.02252
The increasing complexity and cost of clinical trials, particularly in the context of oncology and advanced therapies, pose significant challenges for drug development. This study evaluates the predictive capabilities of large language models (LLMs)
Externí odkaz:
http://arxiv.org/abs/2411.17595
Autor:
Gong, Zheng, Deng, Zhuo, Gan, Run, Niu, Zhiyuan, Chen, Lu, Huang, Canfeng, Liang, Jia, Gao, Weihao, Li, Fang, Zhang, Shaochong, Ma, Lan
The retinal fundus images are utilized extensively in the diagnosis, and their quality can directly affect the diagnosis results. However, due to the insufficient dataset and algorithm application, current fundus image quality assessment (FIQA) metho
Externí odkaz:
http://arxiv.org/abs/2411.12273
Autor:
Zhu, Zichen, Tang, Hao, Li, Yansi, Lan, Kunyao, Jiang, Yixuan, Zhou, Hao, Wang, Yixiao, Zhang, Situo, Sun, Liangtai, Chen, Lu, Yu, Kai
Current mobile assistants are limited by dependence on system APIs or struggle with complex user instructions and diverse interfaces due to restricted comprehension and decision-making abilities. To address these challenges, we propose MobA, a novel
Externí odkaz:
http://arxiv.org/abs/2410.13757