Zobrazeno 1 - 10
of 239
pro vyhledávání: '"Zhang, Xingyao"'
Autor:
Li, Yanshi, Xiong, Shaopan, Chen, Gengru, Li, Xiaoyang, Luo, Yijia, Zhang, Xingyao, Huang, Yanhui, Bu, Xingyuan, Tan, Yingshui, Yuan, Chun, Wang, Jiamang, Su, Wenbo, Zheng, Bo
Reinforcement Learning from Human Feedback (RLHF) has proven highly effective in aligning Large Language Models (LLMs) with human preferences. However, the original RLHF typically optimizes under an overall reward, which can lead to a suboptimal lear
Externí odkaz:
http://arxiv.org/abs/2411.00809
Autor:
Tang, Xiangru, Zhang, Xingyao, Shao, Yanjun, Wu, Jie, Zhao, Yilun, Cohan, Arman, Gong, Ming, Zhang, Dongmei, Gerstein, Mark
Large language models (LLM) excel at a variety of natural language processing tasks, yet they struggle to generate personalized content for individuals, particularly in real-world scenarios like scientific writing. Addressing this challenge, we intro
Externí odkaz:
http://arxiv.org/abs/2406.14275
With the increasing demand for multi-carrier communication in high-mobility scenarios, it is urgent to design new multi-carrier communication waveforms that can resist large delay-Doppler spreads. Various multi-carrier waveforms in the transform doma
Externí odkaz:
http://arxiv.org/abs/2406.02262
We introduce AutoVER, an Autoregressive model for Visual Entity Recognition. Our model extends an autoregressive Multi-modal Large Language Model by employing retrieval augmented constrained generation. It mitigates low performance on out-of-domain e
Externí odkaz:
http://arxiv.org/abs/2402.18695
Autor:
Tang, Xiangru, Zou, Anni, Zhang, Zhuosheng, Li, Ziming, Zhao, Yilun, Zhang, Xingyao, Cohan, Arman, Gerstein, Mark
Large language models (LLMs), despite their remarkable progress across various general domains, encounter significant barriers in medicine and healthcare. This field faces unique challenges such as domain-specific terminologies and reasoning over spe
Externí odkaz:
http://arxiv.org/abs/2311.10537
Previous entity disambiguation (ED) methods adopt a discriminative paradigm, where prediction is made based on matching scores between mention context and candidate entities using length-limited encoders. However, these methods often struggle to capt
Externí odkaz:
http://arxiv.org/abs/2311.03253
Generative approaches powered by large language models (LLMs) have demonstrated emergent abilities in tasks that require complex reasoning abilities. Yet the generative nature still makes the generated content suffer from hallucinations, thus unsuita
Externí odkaz:
http://arxiv.org/abs/2311.03250
Autor:
Lee, Joo Hyung, Park, Wonpyo, Mitchell, Nicole, Pilault, Jonathan, Obando-Ceron, Johan, Kim, Han-Byul, Lee, Namhoon, Frantar, Elias, Long, Yun, Yazdanbakhsh, Amir, Agrawal, Shivani, Subramanian, Suvinay, Wang, Xin, Kao, Sheng-Chun, Zhang, Xingyao, Gale, Trevor, Bik, Aart, Han, Woohyun, Ferev, Milen, Han, Zhonglin, Kim, Hong-Seok, Dauphin, Yann, Dziugaite, Gintare Karolina, Castro, Pablo Samuel, Evci, Utku
This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research. JaxPruner aims to accelerate research on sparse neural networks by providing concise implementations of popular pruning and s
Externí odkaz:
http://arxiv.org/abs/2304.14082
Bayesian Neural Networks (BNNs) that possess a property of uncertainty estimation have been increasingly adopted in a wide range of safety-critical AI applications which demand reliable and robust decision making, e.g., self-driving, rescue robots, m
Externí odkaz:
http://arxiv.org/abs/2110.03553
Publikováno v:
In International Journal of Mechanical Sciences 15 August 2024 276
