Zobrazeno 1 - 10
of 15 249
pro vyhledávání: '"ZHANG, Qin"'
Autor:
Hu, Junhao, Huang, Wenrui, Wang, Haoyi, Wang, Weidong, Hu, Tiancheng, Zhang, Qin, Feng, Hao, Chen, Xusheng, Shan, Yizhou, Xie, Tao
Large Language Models (LLMs) are critical for a wide range of applications, but serving them efficiently becomes increasingly challenging as inputs become more complex. Context caching improves serving performance by exploiting inter-request dependen
Externí odkaz:
http://arxiv.org/abs/2410.15332
Autor:
Bhattarai, Uddhav, Sapkota, Ranjan, Kshetri, Safal, Mo, Changki, Whiting, Matthew D., Zhang, Qin, Karkee, Manoj
Global food production depends upon successful pollination, a process that relies on natural and managed pollinators. However, natural pollinators are declining due to different factors, including climate change, habitat loss, and pesticide use. Thus
Externí odkaz:
http://arxiv.org/abs/2409.19918
One of the major challenges for the agricultural industry today is the uncertainty in manual labor availability and the associated cost. Automated flower and fruit density estimation, localization, and counting could help streamline harvesting, yield
Externí odkaz:
http://arxiv.org/abs/2409.17400
Autor:
Evjemo, Linn Danielsen, Zhang, Qin, Alvheim, Hanne-Grete, Amundsen, Herman Biørn, Føre, Martin, Kelasidi, Eleni
The significant growth in the aquaculture industry over the last few decades encourages new technological and robotic solutions to help improve the efficiency and safety of production. In sea-based farming of Atlantic salmon in Norway, Unmanned Under
Externí odkaz:
http://arxiv.org/abs/2409.15069
Autor:
Kim, Youngeun, Fang, Jun, Zhang, Qin, Cai, Zhaowei, Shen, Yantao, Duggal, Rahul, Raychaudhuri, Dripta S., Tu, Zhuowen, Xing, Yifan, Dabeer, Onkar
The open world is inherently dynamic, characterized by ever-evolving concepts and distributions. Continual learning (CL) in this dynamic open-world environment presents a significant challenge in effectively generalizing to unseen test-time classes.
Externí odkaz:
http://arxiv.org/abs/2409.05312
Autor:
Zhang, Zhan, Zhang, Qin, Jiao, Yang, Lu, Lin, Ma, Lin, Liu, Aihua, Liu, Xiao, Zhao, Juan, Xue, Yajun, Wei, Bing, Zhang, Mingxia, Gao, Ru, Zhao, Hong, Lu, Jie, Li, Fan, Zhang, Yang, Wang, Yiming, Zhang, Lei, Tian, Fengwei, Hu, Jie, Gou, Xin
Publikováno v:
Artificaial Intelligence Review, (2024) 57:151
AI-aided clinical diagnosis is desired in medical care. Existing deep learning models lack explainability and mainly focus on image analysis. The recently developed Dynamic Uncertain Causality Graph (DUCG) approach is causality-driven, explainable, a
Externí odkaz:
http://arxiv.org/abs/2406.05746
High-speed railway stations are crucial junctions in high-speed railway networks. Compared to operations on the tracks between stations, trains have more routing possibilities within stations. As a result, track allocation at a station is relatively
Externí odkaz:
http://arxiv.org/abs/2405.01438
The substantial interest in updating Large Language Models (LLMs) without retraining from scratch is accompanied by several challenges. This is particularly true when updating LLMs with datasets that necessitate domain-expert reasoning across extensi
Externí odkaz:
http://arxiv.org/abs/2403.15736
Open-set graph learning is a practical task that aims to classify the known class nodes and to identify unknown class samples as unknowns. Conventional node classification methods usually perform unsatisfactorily in open-set scenarios due to the comp
Externí odkaz:
http://arxiv.org/abs/2402.18495
Unsupervised question answering is a promising yet challenging task, which alleviates the burden of building large-scale annotated data in a new domain. It motivates us to study the unsupervised multiple-choice question answering (MCQA) problem. In t
Externí odkaz:
http://arxiv.org/abs/2402.17333