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pro vyhledávání: '"Huang, Yingbing"'
Autor:
Huang, Yingbing, Wan, Lily Jiaxin, Ye, Hanchen, Jha, Manvi, Wang, Jinghua, Li, Yuhong, Zhang, Xiaofan, Chen, Deming
Large Language Models (LLMs) have become extremely potent instruments with exceptional capacities for comprehending and producing human-like text in a wide range of applications. However, the increasing size and complexity of LLMs present significant
Externí odkaz:
http://arxiv.org/abs/2406.10903
Autor:
Li, Yuhong, Huang, Yingbing, Yang, Bowen, Venkitesh, Bharat, Locatelli, Acyr, Ye, Hanchen, Cai, Tianle, Lewis, Patrick, Chen, Deming
Large Language Models (LLMs) have made remarkable progress in processing extensive contexts, with the Key-Value (KV) cache playing a vital role in enhancing their performance. However, the growth of the KV cache in response to increasing input length
Externí odkaz:
http://arxiv.org/abs/2404.14469
Adversarial imitation learning (AIL) has stood out as a dominant framework across various imitation learning (IL) applications, with Discriminator Actor Critic (DAC) (Kostrikov et al.,, 2019) demonstrating the effectiveness of off-policy learning alg
Externí odkaz:
http://arxiv.org/abs/2404.08513
Representation learning based on multi-task pretraining has become a powerful approach in many domains. In particular, task-aware representation learning aims to learn an optimal representation for a specific target task by sampling data from a set o
Externí odkaz:
http://arxiv.org/abs/2306.08942
Autor:
Li, Xueyong, Cheng, Yu, Zhang, Bingqing, Chen, Bo, Chen, Yiying, Huang, Yingbing, Lin, Hailing, Zhou, Lili, Zhang, Hui, Liu, Maobai, Que, Wancai, Qiu, Hongqiang
Publikováno v:
Journal of Pharmacokinetics & Pharmacodynamics; Dec2024, Vol. 51 Issue 6, p685-702, 18p
