Zobrazeno 1 - 10
of 4 634
pro vyhledávání: '"Xiong, Hui"'
Autor:
Wang, Jingyao, Guo, Huijie, Qiang, Wenwen, Li, Jiangmeng, Zheng, Changwen, Xiong, Hui, Hua, Gang
Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain regions for dist
Externí odkaz:
http://arxiv.org/abs/2411.06746
With the development of large language models (LLMs), the ability to handle longer contexts has become a key capability for Web applications such as cross-document understanding and LLM-powered search systems. However, this progress faces two major c
Externí odkaz:
http://arxiv.org/abs/2411.02886
Large Language Models (LLMs) have shown remarkable reasoning capabilities on complex tasks, but they still suffer from out-of-date knowledge, hallucinations, and opaque decision-making. In contrast, Knowledge Graphs (KGs) can provide explicit and edi
Externí odkaz:
http://arxiv.org/abs/2410.23875
Virtual Network Embedding (VNE) is a challenging combinatorial optimization problem that refers to resource allocation associated with hard and multifaceted constraints in network function virtualization (NFV). Existing works for VNE struggle to hand
Externí odkaz:
http://arxiv.org/abs/2410.22999
Autor:
Yang, Yiqian, Duan, Yiqun, Jo, Hyejeong, Zhang, Qiang, Xu, Renjing, Jones, Oiwi Parker, Hu, Xuming, Lin, Chin-teng, Xiong, Hui
This paper introduces NeuGPT, a groundbreaking multi-modal language generation model designed to harmonize the fragmented landscape of neural recording research. Traditionally, studies in the field have been compartmentalized by signal type, with EEG
Externí odkaz:
http://arxiv.org/abs/2410.20916
Retrieval module can be plugged into many downstream NLP tasks to improve their performance, such as open-domain question answering and retrieval-augmented generation. The key to a retrieval system is to calculate relevance scores to query and passag
Externí odkaz:
http://arxiv.org/abs/2410.15801
Autor:
Wu, Xingjian, Qiu, Xiangfei, Li, Zhengyu, Wang, Yihang, Hu, Jilin, Guo, Chenjuan, Xiong, Hui, Yang, Bin
Anomaly detection in multivariate time series is challenging as heterogeneous subsequence anomalies may occur. Reconstruction-based methods, which focus on learning nomral patterns in the frequency domain to detect diverse abnormal subsequences, achi
Externí odkaz:
http://arxiv.org/abs/2410.12261
Despite the remarkable success of Large Language Models (LLMs), evaluating their outputs' quality regarding preference remains a critical challenge. Existing works usually leverage a powerful LLM (e.g., GPT4) as the judge for comparing LLMs' output p
Externí odkaz:
http://arxiv.org/abs/2410.12869
Real estate appraisal is important for a variety of endeavors such as real estate deals, investment analysis, and real property taxation. Recently, deep learning has shown great promise for real estate appraisal by harnessing substantial online trans
Externí odkaz:
http://arxiv.org/abs/2410.08947
Autor:
Yan, Yibo, Wang, Shen, Huo, Jiahao, Li, Hang, Li, Boyan, Su, Jiamin, Gao, Xiong, Zhang, Yi-Fan, Xu, Tianlong, Chu, Zhendong, Zhong, Aoxiao, Wang, Kun, Xiong, Hui, Yu, Philip S., Hu, Xuming, Wen, Qingsong
As the field of Multimodal Large Language Models (MLLMs) continues to evolve, their potential to revolutionize artificial intelligence is particularly promising, especially in addressing mathematical reasoning tasks. Current mathematical benchmarks p
Externí odkaz:
http://arxiv.org/abs/2410.04509