Zobrazeno 1 - 10
of 446
pro vyhledávání: '"Zhang Linjun"'
Autor:
Zhang Linjun
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 8, Iss 1, Pp 3217-3228 (2023)
This paper uses fractional differential equations to evaluate the mental health of college students. The author first proposes different emotional possibility spaces, such as emotional energy, emotional intensity, and emotional entropy. Secondly, thi
Externí odkaz:
https://doaj.org/article/7fd1a87e24494ce0a4c92badb1f02d08
The propensity of Large Language Models (LLMs) to generate hallucinations and non-factual content undermines their reliability in high-stakes domains, where rigorous control over Type I errors (the conditional probability of incorrectly classifying h
Externí odkaz:
http://arxiv.org/abs/2411.02603
Autor:
Hou, Xiaotian, Zhang, Linjun
Algorithmic fairness in machine learning has recently garnered significant attention. However, two pressing challenges remain: (1) The fairness guarantees of existing fair classification methods often rely on specific data distribution assumptions an
Externí odkaz:
http://arxiv.org/abs/2410.16477
Autor:
Xia, Peng, Zhu, Kangyu, Li, Haoran, Wang, Tianze, Shi, Weijia, Wang, Sheng, Zhang, Linjun, Zou, James, Yao, Huaxiu
Artificial Intelligence (AI) has demonstrated significant potential in healthcare, particularly in disease diagnosis and treatment planning. Recent progress in Medical Large Vision-Language Models (Med-LVLMs) has opened up new possibilities for inter
Externí odkaz:
http://arxiv.org/abs/2410.13085
Autor:
Zhong, Yibo, Jiang, Haoxiang, Li, Lincan, Nakada, Ryumei, Liu, Tianci, Zhang, Linjun, Yao, Huaxiu, Wang, Haoyu
Fine-tuning pre-trained models is crucial for adapting large models to downstream tasks, often delivering state-of-the-art performance. However, fine-tuning all model parameters is resource-intensive and laborious, leading to the emergence of paramet
Externí odkaz:
http://arxiv.org/abs/2410.01870
Autor:
Xia, Peng, Zhu, Kangyu, Li, Haoran, Zhu, Hongtu, Li, Yun, Li, Gang, Zhang, Linjun, Yao, Huaxiu
The recent emergence of Medical Large Vision Language Models (Med-LVLMs) has enhanced medical diagnosis. However, current Med-LVLMs frequently encounter factual issues, often generating responses that do not align with established medical facts. Retr
Externí odkaz:
http://arxiv.org/abs/2407.05131
Autor:
Xu, Zexing, Zhang, Linjun, Yang, Sitan, Etesami, Rasoul, Tong, Hanghang, Zhang, Huan, Han, Jiawei
Demand prediction is a crucial task for e-commerce and physical retail businesses, especially during high-stake sales events. However, the limited availability of historical data from these peak periods poses a significant challenge for traditional f
Externí odkaz:
http://arxiv.org/abs/2406.16221
Imbalanced data and spurious correlations are common challenges in machine learning and data science. Oversampling, which artificially increases the number of instances in the underrepresented classes, has been widely adopted to tackle these challeng
Externí odkaz:
http://arxiv.org/abs/2406.03628
The development of generative language models that can create long and coherent textual outputs via autoregression has lead to a proliferation of uses and a corresponding sweep of analyses as researches work to determine the limitations of this new p
Externí odkaz:
http://arxiv.org/abs/2406.06581
Autor:
Zhou, Yiyang, Fan, Zhiyuan, Cheng, Dongjie, Yang, Sihan, Chen, Zhaorun, Cui, Chenhang, Wang, Xiyao, Li, Yun, Zhang, Linjun, Yao, Huaxiu
Large Vision-Language Models (LVLMs) have made substantial progress by integrating pre-trained large language models (LLMs) and vision models through instruction tuning. Despite these advancements, LVLMs often exhibit the hallucination phenomenon, wh
Externí odkaz:
http://arxiv.org/abs/2405.14622