Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Zhang Qingru"'
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
The current lack of music core literacy in vocal music teaching needs to be solved, and this paper aims to improve this problem. In the paper, a hybrid attention module is added to the multi-channel of MFCC to extract the acoustic elements of the mus
Externí odkaz:
https://doaj.org/article/a5d1f9bf84c34565900333bddc238666
Autor:
Zhang, Qingru, Yu, Xiaodong, Singh, Chandan, Liu, Xiaodong, Liu, Liyuan, Gao, Jianfeng, Zhao, Tuo, Roth, Dan, Cheng, Hao
Large language models (LLMs) have demonstrated remarkable performance across various real-world tasks. However, they often struggle to fully comprehend and effectively utilize their input contexts, resulting in responses that are unfaithful or halluc
Externí odkaz:
http://arxiv.org/abs/2409.10790
Autor:
Bukharin, Alexander, Hong, Ilgee, Jiang, Haoming, Li, Zichong, Zhang, Qingru, Zhang, Zixuan, Zhao, Tuo
Reinforcement learning from human feedback (RLHF) provides a principled framework for aligning AI systems with human preference data. For various reasons, e.g., personal bias, context ambiguity, lack of training, etc, human annotators may give incorr
Externí odkaz:
http://arxiv.org/abs/2406.15568
Autor:
Kang, Hao, Zhang, Qingru, Kundu, Souvik, Jeong, Geonhwa, Liu, Zaoxing, Krishna, Tushar, Zhao, Tuo
Key-value (KV) caching has become the de-facto to accelerate generation speed for large language models (LLMs) inference. However, the growing cache demand with increasing sequence length has transformed LLM inference to be a memory bound problem, si
Externí odkaz:
http://arxiv.org/abs/2403.05527
In human-written articles, we often leverage the subtleties of text style, such as bold and italics, to guide the attention of readers. These textual emphases are vital for the readers to grasp the conveyed information. When interacting with large la
Externí odkaz:
http://arxiv.org/abs/2311.02262
Pretrained transformer models have demonstrated remarkable performance across various natural language processing tasks. These models leverage the attention mechanism to capture long- and short-range dependencies in the sequence. However, the (full)
Externí odkaz:
http://arxiv.org/abs/2310.12442
Autor:
Bukharin, Alexander, Li, Yan, Yu, Yue, Zhang, Qingru, Chen, Zhehui, Zuo, Simiao, Zhang, Chao, Zhang, Songan, Zhao, Tuo
Multi-Agent Reinforcement Learning (MARL) has shown promising results across several domains. Despite this promise, MARL policies often lack robustness and are therefore sensitive to small changes in their environment. This presents a serious concern
Externí odkaz:
http://arxiv.org/abs/2310.10810
Publikováno v:
Jixie chuandong, Vol 41, Pp 128-130 (2017)
By using the function of eigen buckling in ANSYS,the torsional buckling problem of flexspline in a harmonic driver is analyzed. Through changing the structure parameter of two kinds of flexspline,the corresponding critical instability torque under ea
Externí odkaz:
https://doaj.org/article/df7eda9c95594dd5b1cb880d9a1e1502
LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation
Transformer models have achieved remarkable results in various natural language tasks, but they are often prohibitively large, requiring massive memories and computational resources. To reduce the size and complexity of these models, we propose LoSpa
Externí odkaz:
http://arxiv.org/abs/2306.11222
Autor:
Zhang, Qingru, Chen, Minshuo, Bukharin, Alexander, Karampatziakis, Nikos, He, Pengcheng, Cheng, Yu, Chen, Weizhu, Zhao, Tuo
Fine-tuning large pre-trained language models on downstream tasks has become an important paradigm in NLP. However, common practice fine-tunes all of the parameters in a pre-trained model, which becomes prohibitive when a large number of downstream t
Externí odkaz:
http://arxiv.org/abs/2303.10512