Zobrazeno 1 - 10
of 23 346
pro vyhledávání: '"Wei-Ping An"'
Autor:
Yang, Chih-Kai, Fu, Yu-Kuan, Li, Chen-An, Lin, Yi-Cheng, Lin, Yu-Xiang, Chen, Wei-Chih, Chung, Ho Lam, Kuan, Chun-Yi, Huang, Wei-Ping, Lu, Ke-Han, Lin, Tzu-Quan, Wang, Hsiu-Hsuan, Hu, En-Pei, Hsu, Chan-Jan, Tseng, Liang-Hsuan, Chiu, I-Hsiang, Sanga, Ulin, Chen, Xuanjun, Hsu, Po-chun, Yang, Shu-wen, Lee, Hung-yi
This technical report presents our initial attempt to build a spoken large language model (LLM) for Taiwanese Mandarin, specifically tailored to enable real-time, speech-to-speech interaction in multi-turn conversations. Our end-to-end model incorpor
Externí odkaz:
http://arxiv.org/abs/2411.07111
Formal proofs are challenging to write even for experienced experts. Recent progress in Neural Theorem Proving (NTP) shows promise in expediting this process. However, the formal corpora available on the Internet are limited compared to the general t
Externí odkaz:
http://arxiv.org/abs/2410.15748
Autor:
Wei, Ping, Zou, Qing-Song
In this paper, we analyze any-order Runge-Kutta spectral volume schemes (RKSV(s,k)) for solving the one-dimensional scalar hyperbolic equation. The RKSV(s,k) was constructed by using the $s$-th explicit Runge-Kutta method in time-discretization which
Externí odkaz:
http://arxiv.org/abs/2409.13485
Autor:
Li, Guang-Shuai, Su, Jun, Terashima, Satoru, Zhao, Jian-Wei, Xiao, Er-Xi, Zhang, Ji-Chao, He, Liu-Chun, Guo, Ge, Lin, Wei-Ping, Lin, Wen-Jian, Liu, Chuan-Ye, Lu, Chen-Gui, Mei, Bo, Pang, Dan-Yang, Sun, Ye-Lei, Sun, Zhi-Yu, Wang, Meng, Wang, Feng, Wang, Jing, Wang, Shi-Tao, Wei, Xiu-Lin, Xu, Xiao-Dong, Xu, Jun-Yao, Zhu, Li-Hua, Zheng, Yong, Zhang, Mei-Xue, Zhang, Xue-Heng
Publikováno v:
Physics Letters B 859 (2024) 139143
We report on the first measurement of the elemental fragmentation cross sections (EFCSs) of $^{29-33}\mathrm{Si}$ on a carbon target at $\sim$230~MeV/nucleon. The experimental data covering charge changes of $\Delta Z$ = 1-4 are reproduced well by th
Externí odkaz:
http://arxiv.org/abs/2407.14697
This paper presents a general scheme for enhancing the convergence and performance of DETR (DEtection TRansformer). We investigate the slow convergence problem in transformers from a new perspective, suggesting that it arises from the self-attention
Externí odkaz:
http://arxiv.org/abs/2407.11699
In this work, we introduce Speech-Copilot, a modular framework for instruction-oriented speech-processing tasks that minimizes human effort in toolset construction. Unlike end-to-end methods using large audio-language models, Speech-Copilot builds sp
Externí odkaz:
http://arxiv.org/abs/2407.09886
Deep Learning-based end-to-end Automatic Speech Recognition (ASR) has made significant strides but still struggles with performance on out-of-domain samples due to domain shifts in real-world scenarios. Test-Time Adaptation (TTA) methods address this
Externí odkaz:
http://arxiv.org/abs/2406.11064
Large audio-language models (LALMs) enhance traditional large language models by integrating audio perception capabilities, allowing them to tackle audio-related tasks. Previous research has primarily focused on assessing the performance of LALMs acr
Externí odkaz:
http://arxiv.org/abs/2406.08402
Video moment retrieval and highlight detection are two highly valuable tasks in video understanding, but until recently they have been jointly studied. Although existing studies have made impressive advancement recently, they predominantly follow the
Externí odkaz:
http://arxiv.org/abs/2404.09263
DETR-like methods have significantly increased detection performance in an end-to-end manner. The mainstream two-stage frameworks of them perform dense self-attention and select a fraction of queries for sparse cross-attention, which is proven effect
Externí odkaz:
http://arxiv.org/abs/2403.16131